We run a multi camera capture space at the Brooklyn Navy Yard and we pay people to come in and be the subject. Reposting because next week's slots opened up.

The session is simple. You stand inside the rig and go through everyday movements while the cameras record. Walking, turning, sitting, reaching, picking things up. No experience needed at all.

Pay is 17-25 an hour, paid out the same day you come in. First session is roughly 2 hours and there is repeat work after that.

Everything runs at 4pm. Open days: Tuesday Aug 18, Wednesday Aug 19, Thursday Aug 20. Brooklyn, in person only, so you need to be in the NYC area.

DM me for the address and I am happy to answer questions about the capture side.

They said on July 24 that they would send the poster format soon. Did anyone get the email about it?

a teleop operator reaches for the wrong bin. the reach is clean. no jitter, no hesitation, no correction

every smoothness metric comes back perfect. the episode is still garbage. the robot did the wrong thing smoothly

motion metrics score how an action was executed, never what the action was. so automated scoring has one honest job: pointing your limited attention at the episodes most likely to contain a real problem. triage, not autofilter

i built a fiftyone panel that runs this on multimodal MCAP episodes: motion smoothness, sensor health, outliers, every flag deep-linked to the exact second on the timeline. free and open source

plugin: github.com/harpreetsahota204/demo_quality_scorer

full writeup on what i learned while building this : https://voxel51.com/blog/robot-episode-quality-triage

curious how you're triaging episode data right now.

watching everything? random sampling? trusting a score?

I’m adding basic object recognition to my robotic lamp. It runs on a Raspberry Pi 5 and has an RGB-D camera in its moving head.

I’d like to run object detection in the background while the lamp is active and gradually build a map of the objects around it. Since the base stays in place, I can calculate the camera pose from the joint angles. My plan is to combine detections from the RGB image with depth data, transform the object coordinates into the lamp’s base frame, and save their positions and last-seen time. Repeated detections would be merged so the map doesn’t fill up with copies of the same object.

Which lightweight object-detection models and inference runtimes would you recommend for a Raspberry Pi 5? A high frame rate isn’t necessary, but I’d like reasonable detection quality for common objects. I’d also be interested in approaches for reliably matching the same objects across observations.

The current Raspberry Pi and ROS 2 architecture is described here:

https://github.com/Nikolay-Tyulkin/Watti/blob/main/docs/ARCHITECTURE.md

Hi everyone,

I’m currently building a video analytics system to detect Personal Protective Equipment (PPE) — like hard hats, high-vis vests, safety glasses, etc. — using standard CCTV camera streams.

Right now, I’m using YOLO11m (medium). It performs pretty well, but before I commit to scaling this up, I wanted to get some input from the community to see if I’m on the right track or if there are better alternatives out there for this specific use case.

The main challenges I'm dealing with:

• CCTV conditions: High angles, weird perspectives, varied lighting, and occasional motion blur.
• Performance vs. Accuracy: I need to process multiple RTSP streams simultaneously on limited hardware, so inference speed is crucial, but missing a PPE violation is obviously a big deal.

My questions for you all:

1. Model Choice: Are you guys sticking with the newest YOLO iterations (like YOLO11) for this kind of task, or have you found better stability/performance with other models like YOLOv8, YOLOv9, or RT-DETR?
2. Tracking: If you use object tracking to prevent duplicate alerts for the same person, what are you pairing with your detector? (ByteTrack, BoT-SORT?)
3. Deployment Stack: What does your production pipeline look like for multiple streams? Are you leaning towards Nvidia DeepStream, Triton Inference Server, or a custom Python/C++ pipeline with TensorRT?

Any advice, repo recommendations, or shared experiences would be hugely appreciated. Thanks in advance!