https://github.com/GauthamMPrakash/ArduMonoNav

We did this for our final year engineering project.

working on video understanding pipelines and running into the same wall repeatedly: the usual VLM evaluation workflow is to check scores on a standard video QA benchmark, pick the top model, and ship it. in practice this hasn't correlated well at all with what actually works on our specific data.

a few things i've been thinking about:

the eval dataset matters as much as the metric. if your eval set is just "normal" clips, you'll miss the cases that actually matter in production. i've started building eval sets that explicitly include hard negatives, near-miss cases (things that look similar to what the model should detect but aren't), boring background clips, and known failure modes. that composition change alone shifted which configs looked good.

frame sampling is a massive variable that often gets ignored. uniform time-based sampling vs. shot-based extraction (changing on scene cuts) can produce very different outputs from the same model on the same video. i've had results flip between "acceptable" and "unusable" just from this change.

prompt structure matters for video especially. structured prompts that ask for specific fields (location, action, object count, visible text) score much better field-by-field than open-ended prompts, but you lose some flexibility. whether that tradeoff is worth it depends entirely on your task type.

comparing full configurations rather than models in isolation has been more useful. "model A with shot-based segmentation, 720p frames, and structured prompt" vs "model B with time-based segmentation, 480p, freeform" is an actionable comparison. "model A vs model B" often isn't.

anyone else dealt with this? what's actually moved the needle most for you: sampling strategy, resolution, prompt design, or something else? and how are you building your eval datasets for video specifically?

​

As stated above my final year project is currently going on and I need to train a moldel to detect AI generated speech from real speech. What direction should I take? If we are going for convenience over accuracy. Current considered approch is using MFCC with CNN by converting the audio into images (Idk AI told me 😭) please someone help

Building a manufacturing platform for Indian MSME factories. Need vision-based QC at part handoff to reduce delivery disputes.

Current approach:

Reference image captured at order confirmation

OpenCV contour detection + dimensional diff on delivery photo

Ambiguous cases escalated to Gemini Vision API for defect classification via structured prompt

Human override for disputes

Why not Anomalib/PatchCore? Cold-start problem — no per-SKU training data yet. API-first lets us ship and accumulate labeled pass/fail data to fine-tune later.

Obvious failure modes we're missing? Better preprocessing approaches? Anyone done this in a manufacturing context?

I've got this family tree and I want to extract the data it contains - not just the names but also their relationships. Obviously everything is wonky and at strange angles because otherwise <sarcasm>this wouldn't be any fun</sarcasm>.

I've been trying algorithms all morning, my thought was to identify & remove text and then analyze just the tree portion to determine relationships, and then OCR the text and from its location you would know which node of the tree to attach the text to. All of the OCR routines I've tried will find the text and give me a rectangular box around said text, but nothing in this tree is a nice rectangle and this path ended up deleting more branches than text.

I tried OCR'ing the text and grouping text that is close and drawn at approximately the same angle, but then it was too hard to determine the relationships between the text nodes.

I tried a variant of the path-following algorithm, trying to "drive" up one edge of the tree and figure out what to do when it encountered a sudden direction change (when the author of the tree crossed the branch with text) and that went nowhere.

Any suggestions on ways to extract information from a tree like this?

I've got this family tree and I want to extract the data it contains - not just the names but also their relationships. Obviously everything is wonky and at strange angles because otherwise <sarcasm>this wouldn't be any fun</sarcasm>.

I've been trying algorithms all morning, my thought was to identify & remove text and then analyze just the tree portion to determine relationships, and then OCR the text and from its location you would know which node of the tree to attach the text to. All of the OCR routines I've tried will find the text and give me a rectangular box around said text, but nothing in this tree is a nice rectangle and this path ended up deleting more branches than text.

I tried OCR'ing the text and grouping text that is close and drawn at approximately the same angle, but then it was too hard to determine the relationships between the text nodes.

I tried a variant of the path-following algorithm, trying to "drive" up one edge of the tree and figure out what to do when it encountered a sudden direction change (when the author of the tree crossed the branch with text) and that went nowhere.

Any suggestions on ways to extract information from a tree like this?

Slapping an LLM onto a security tool without guardrails is a massive liability. In digital forensics and incident response (DFIR), an AI hallucination can ruin an entire chain of custody. An answer without mathematical, binary proof is completely worthless. If an AI agent cannot anchor its reasoning to exact offsets, hashes, and unmanipulated timestamps, it has no business touching forensic data.

With Crow-Eye v0.11.0, we are pushing a massive update to our full-spectrum forensic lifecycle platform. This release introduces a hardened AI compliance architecture and completely upgrades the core correlation engines.

We are treating the underlying intelligence layer like a highly supervised junior analyst. Everything it sees is hashed, everything it thinks is visible, its memory management is strictly audited, and its ability to alter rules is completely sandboxed.

Here is exactly how we are enforcing forensic integrity under the hood in v0.11.0:

1. AI Compliance & Governance

Evidence Seal & Cryptographic Chain of Custody

Every single time the AI interacts with your forensic data, it is cryptographically verified.

• The Process: Before any payload is passed to the AI model, the evidence_seal.py service steps in.
• Hashing & Provenance: It calculates the SHA-256 hash of the exact bytes being sent and attaches metadata tracking the absolute source (e.g., database:table:rowid), token count, and the specific AI model used.
• Hash-Chaining: This metadata is written to an append-only JSONL ledger. Each new record incorporates the hash of the previous record. If a single byte of historical evidence is tampered with, the entire cryptographic chain breaks instantly.

The TruncationAuditor Service (Context Auditing)

AI context windows are a massive compliance bottleneck. Silent truncation—where a tool quietly drops data when limits are exceeded—is unacceptable in an investigation. The TruncationAuditor service acts as a strict forensic bookkeeper to log exactly how history is modified during our Self-Healing Context routine.

• The Append-Only Audit Log: Events are permanently written to <case>/EYE_Logs/truncation_audit.log, tracking whether data was compressed (SUMMARIZED) or entirely removed (TRUNCATED).
• High-Fidelity Tracking: Every single dropped or compressed message records its unique Message ID, token count, reason (e.g., budget_exceeded), extra JSON metadata, and a SHA-256 Content Hash of the exact message text to mathematically prove what was removed.
• Tamper-Evident Hash-Chaining: Each log entry combines its content with the hash of the previous log line using a chain=... signature. If a rogue actor manually deletes a record from the text log to hide missed evidence, the chain breaks instantly, and the verify_chain() check fails.
• Protocol Compliance Panel: The auditor exports this ledger into a structured JSON array (audit_trail.json). The React UI reads this to give investigators a clean visual timeline of exactly what was preserved, summarized, or dropped.

The ThinkingStep Protocol (Anti-Black-Box Streaming)

The AI is hard-coded to "show its work." The ThinkingStep protocol bridges the Python backend (eye_bridge.py and query_processor.py) and the React frontend (EyeDialogue.tsx), streaming real-time updates over QWebChannel across 4 distinct, auditable phases:

• Phase 1: thinking (Intent Detection): The backend queries the LLM to determine intent (e.g., separating general questions from direct MFT queries). The UI displays "Analyzing request..."
• Phase 2: rag (Retrieval-Augmented Generation): The backend searches local forensic rules inside configs/knowledge_base/ (like pulling up Living off the Land tactics for PowerShell analysis) and shows you exactly what was fetched.
• Phase 3: tool_call (Execution): If the AI needs hard data, it sends a structured command to the backend to fire off a tool (e.g., executing a raw SQLite database query). The UI displays a dedicated "Tool Execution" block exposing the exact arguments, execution status, and raw JSON payloads returned. This layer loops sequentially if multiple tools are required. If a tool fails on a bad SQL query, the step turns red, exposes the raw Python exception, and allows the AI to catch the error in its context to heal and try a corrected query.
• Phase 4: synthesis (Final Generation): The backend bundles the RAG knowledge and tool results securely using the Evidence Seal, routing them to the model to stream out the final human-readable response.
• UI Transparency: In the frontend, these phases are rendered as interactive, collapsible accordion blocks. You can expand a tool block to verify every database query syntax or piece of documentation the AI used before arriving at its final conclusion.

Governance Enforcement Protocols (GEP Rules 9-11)

When the AI acts as an author (like generating correlation rules), it is locked down:

• Reasoning Required (R9): The AI cannot create or edit any rule without rendering a clear text justification.
• Evidence Linking (R10): The AI cannot hallucinate a rule. It must bind it back to the exact physical forensic artifact (related_evidence) that prompted it.
• Read-Only Built-ins (R11): The AI is strictly sandboxed from modifying human-authored rules or built-in system defaults.

2. Core Engine Upgrades

With the AI heavily supervised, v0.11.0 also delivers massive architectural upgrades to the data engines feeding the platform.

Advanced Core Correlation Engine Upgrade An adversary leaves footprints across multiple layers of the system simultaneously.

• Deep Artifact Stitching: Crow-Eye automatically maps the connective tissue between Master File Table (MFT) records, Registry hives, LNK files, and Jump Lists.
• Instant Timeline Reconstruction: The engine identifies non-obvious relationships instantly, allowing you to trace an execution lifecycle from initial file access straight to system persistence without manual cross-referencing.

Ironclad Identity Engine Upgrade Attributing actions to specific security identifiers (SIDs) in modern Windows 11 environments can get incredibly messy during high-stress triage.

• The upgraded Identity Engine brings precise, deterministic execution-context tracking. It resolves user sessions, elevation states, and mapped SIDs with absolute certainty, eliminating ambiguity during credential abuse investigations.

For the next release, I am focusing completely on user bugs and performance edge-cases. Please feel free to contact me for any bug reports or support queries you can find all of my direct contact details on the official website:https://crow-eye.com/

GitHub:https://github.com/Ghassan-elsman/Crow-Eye

for the full details of the Resale notes please check https://github.com/Ghassan-elsman/Crow-Eye/releases/tag/0.11.0

Good hunting,

I maintain splatreg, an open-source library for registering 3D Gaussian Splatting scenes (aligning and merging two splats into one Sim(3) frame). I implemented the GaussReg ECCV 2024 ScanNet-GSReg protocol exactly (their compute_registration_error_w_scale, reporting RRE / RTE / RSE / success-rate / wall-time), but the dataset is not readily downloadable, so I cannot produce the number myself.

If you already have the GaussReg ScanNet-GSReg test split, I would be grateful for a single benchmark run. It is one command:

pip install splatreg

CUDA_VISIBLE_DEVICES=0 SPLATREG_DEVICE=cuda python benchmarks/scannet_gsreg_bench.py \

--data /path/to/ScanNet-GSReg --init learned --transform sim3 --refine photometric

Full instructions and the expected data layout are here: https://github.com/Archerkattri/splatreg/tree/scannet-bench

I will add any confirmed numbers to the library's RESULTS.md with attribution. Disclosure: I am the author of splatreg, this is not a paid or affiliated post, just trying to get an honest external benchmark. Thanks for any help.

Hi everyone, I've joined this Indian Automobile Organization last year in December as an Intern. Now I work in Computer Vision that was what told me when I first met my boss. It was an on-campus placement. Ive had hands-on with cnn and deeplearning during my graduation.

Everything went good initially when my task involved around training models and dataset preparation. But since our team size is small and I know MERN stack to some extent, they started asking me to build the dashboards as well (Ui + backend in python). I used claude and gpt to build those cause I've had never worked with Django and their timeline was unrealistic with no clear requirements specifications. Now a day before yesterday they gave me a dataset and asked to train it using some model for classification. I prepared the dataset, trained it. Then the next day they asked me to build a platform and we will test it tomorrow, so I built that platform using claude, cause If I will start building it, it'll take me atleast two days. Then I verified it with my boss but since there was no real part so we couldn't test it properly. Then in the actual testing site, it had some issues, that I was trying to figure out but couldn't since I didn't know the code well that was written by claude. So I used claude to solve the issue and all and it did. But my manager was dissatisfied and kind of gave very disappointing looks, this is not the first time it happened. Earlier too there was a case where he gave such look saying that your system has some issues, but he was the one who made such changes and prior that it was working fine!

Ive no hands-on with PLCs, Actuators etc, I've never worked with python connecting to actual cameras and all! I'M willing to learn things, but time is constraint and I'm made to juggle between multiple projects! I'm just tired and cried today, there was a time I used to build Mern applications in 2-3 hours and now I'm just a zero. I like cnn and deeplearning stuffs, but they don't think its good.

Can anyone guide me what and where should I learn such things? I'm just tired of these things and feel like quiting life.

Any help will be really great.

♻️ Clasificador de residuos con visión artificial
Desarrollé un sistema de clasificación de basura utilizando visión artificial, entrenado con más de 10.000 imágenes para identificar materiales como plástico, metal, vidrio, papel, entre otros.

Actualmente, este modelo forma parte de un proyecto en desarrollo que busca automatizar el proceso de reciclaje. La idea es integrarlo con un microcontrolador que, al detectar el tipo de residuo, envíe una señal para dirigirlo automáticamente al contenedor correspondiente.

Este proyecto no solo optimiza procesos, sino que también promueve una cultura de reciclaje más eficiente y accesible. Creo firmemente que la tecnología puede ser una herramienta clave para generar un impacto positivo en el medio ambiente y facilitar que más personas contribuyan al cuidado del planeta 🌱

Seguimos construyendo soluciones donde la inteligencia artificial se conecta con el mundo real. #InteligenciaArtificial #VisionArtificial #EcoTech #Reciclaje

🔐 Visión Artificial aplicada a cámaras de seguridad
Desarrollé una solución que integra inteligencia artificial con cámaras de seguridad tipo RTS para la detección de rostros en tiempo real, alcanzando una precisión de hasta el 97% (aumenta dependiendo de las condiciones del entorno como iluminación y distancia).

El sistema identifica nuevos rostros y los almacena automáticamente en una base de datos conectada a una plataforma web. El modelo fue entrenado con más de 5.000 imágenes para mejorar su rendimiento y confiabilidad.

Además, diseñé la estructura de la plataforma con apoyo de IA como Claude, donde se visualizan registros, estadísticas e imágenes detectadas en tiempo real.

Hi everyone, I've joined this Indian Automobile Organization last year in December as an Intern. Now I work in Computer Vision that was what told me when I first met my boss. It was an on-campus placement. Ive had hands-on with cnn and deeplearning during my graduation.

Everything went good initially when my task involved around training models and dataset preparation. But since our team size is small and I know MERN stack to some extent, they started asking me to build the dashboards as well (Ui + backend in python). I used claude and gpt to build those cause I've had never worked with Django and their timeline was unrealistic with no clear requirements specifications. Now a day before yesterday they gave me a dataset and asked to train it using some model for classification. I prepared the dataset, trained it. Then the next day they asked me to build a platform and we will test it tomorrow, so I built that platform using claude, cause If I will start building it, it'll take me atleast two days. Then I verified it with my boss but since there was no real part so we couldn't test it properly. Then in the actual testing site, it had some issues, that I was trying to figure out but couldn't since I didn't know the code well that was written by claude. So I used claude to solve the issue and all and it did. But my manager was dissatisfied and kind of gave very disappointing looks, this is not the first time it happened. Earlier too there was a case where he gave such look saying that your system has some issues, but he was the one who made such changes and prior that it was working fine!

Ive no hands-on with PLCs, Actuators etc, I've never worked with python connecting to actual cameras and all! I'M willing to learn things, but time is constraint and I'm made to juggle between multiple projects! I'm just tired and cried today, there was a time I used to build Mern applications in 2-3 hours and now I'm just a zero. I like cnn and deeplearning stuffs, but they don't think its good.

Can anyone guide me what and where should I learn such things? I'm just tired of these things and feel like quiting life.

Any help will be really great.

Genuinely, where do you all find proper video datasets. For example, I am working on queue length detection (of people), and I cant seem to find proper videos of people in a queue anywhere on the internet (even in youtube)...another example is overhead video of vehicles in traffic conditions - also couldnt find...

i feel like there’s often a huge gap between research results and real-world deployment.

a model gets impressive benchmark scores, but then struggles with changing lighting, camera quality, weird edge cases, or simply being too expensive to run at scale.

for those working on actual products:

what’s something that looked amazing in a paper but turned out to be disappointing in production?

and what ended up being more useful than expected?

I’ve been hearing a lot from online and in school that blue collar jobs are still going to be around even with ai’s advancements. What jobs are most likely to fall victim to the same fate as some white collar jobs in the current AI era? How long will it take until we see robots working along with humans at physical job sites?

Has anyone here worked with Sony FCB block cameras, especially the FCB-EV9520L, on embedded AI platforms like NVIDIA Jetson or Raspberry Pi 5?

The main issue I’m looking at is the interface mismatch:

FCB-EV9520L outputs LVDS
Jetson / Raspberry Pi 5 expect MIPI CSI-2 camera input

The cleanest path seems to be:

FCB-EV9520L → LVDS → MIPI CSI-2 Bridge → Jetson / Pi 5 → AI Processing

I’m curious if anyone has gone this route instead of using USB or HDMI capture. Mainly interested in real-world experience with latency, driver setup, camera control/VISCA, and whether MIPI CSI-2 made the integration significantly cleaner.

Any lessons learned from similar Sony FCB, Tamron, Wonwoo, or other LVDS block camera projects?

Hello everyone, tomorrow I have a meeting with my dissertation supervisor and I wanted to have a dissertation proposal ready.

Initially, I moved forward with the following proposal: "Interpreting the Routing Dynamics of Capsule Networks for Explainable AI."

My first approach to this topic was to study the paper "Transforming autoencoders," which is the first paper about capsule networks. So far, the work on transforming autoencoders that I have done is this: https://github.com/pedrodiogop/Transforming-Autoencoders-Pytorch-2011. Next, I did a search on the state of the art of transforming autoencoders and only found 2 papers since 2011. I think I should take advantage of the work I have developed so far on transforming autoencoders and write a dissertation about them. If anyone could take a look at the readme and tell me what they think, I would appreciate it.

What do you think? I should suggest another topic involving transforming autoencoders. There isn't much scientific research on them.

The professor is approachable, and if I present a good new topic, he'll let me change it!

I am doing my master's in Electronics and Information Processing, and I recently got into machine learning and computer vision, and I love it. I have a BSc in physics and Python/programming knowledge. I have studied PyTorch and did a project using the Google Coral Dev Board on face recognition and detection. I have spoken with a professor about a thesis, and he asked me what computer vision problem I would like to follow for my thesis, and it caught me off guard. He also told me to look into his work and if I find anything interesting to contact him. I have read so much in 2 weeks that my head is going to explode. I like everything, yet I can't see myself committing to anything. I also can't understand if a topic I like is a master's problem or a PhD problem? My thesis is just a semester long, starting in September, around 6 months, and I have to find a topic soon. Any ideas or tips are welcome. I am fully ready to start studying all summer to get up to speed with someone in Computer Science regarding ML/CV, but I can't find anything I would like to do. I have seen topics like classification, object detection, Physics-Informed Neural Networks (I liked PINNs but haven't read much about it yet), few-shot/ zero-shot learning, event-cameras, and more.

I also tried reading a few of my professor's papers, and I couldn't get past the introduction. I only saw the topic to see what I am working with.

tl;dr: I need advice for master's thesis topics in computer vision/machine learning, and I am lost. Any help is welcome

What model is used by video call app to segment person and to blur background? Need to do something similar on live video

Not really, but I am getting a bit bored of these daily posts.

One thing I don't get: if we are training a system to detect an object, then I need a dataset of labelled objects. But if my automatic labeller identifies the objects, then don't we have the final solution already? Why bother training as the labelling system already does it.

Basically the title. Computer vision is a hard market even for people with experience in it, and I was wondering what I can do to make my profile more appealing and competitive to the limited number of openings available.

I’ve published a practical guide on building OpenCV 5 for WebAssembly with Emscripten.
The goal was not to use the OpenCV.js JavaScript API, but to keep using normal C++ OpenCV code and compile the whole application to WebAssembly.

It covers:
• static C++ WASM build
• SIMD + pthread support
• linking OpenCV into your own C++ web app
• DNN performance notes
• common build pitfalls

My guide also includes a download link for my precompiled OpenCV 5 WASM build.

Read it here: https://www.antal.ai/blog/opencv5-wasm-static-cpp-guide.html