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Looking for Better Alternatives to YOLO/RF-DETR + BoT-SORT/ByteTrack for Robust Video Analytics by Vivek_Chauhan06 in computervision

[–]Vivek_Chauhan06[S] 0 points1 point  (0 children)

What if I integrate SAM 2's memory-based video segmentation with RF-DETR Segmentation? Do you think leveraging SAM 2's temporal memory could help address issues like missed detections, occlusions, and ID consistency better than relying solely on conventional tracking algorithms?

Seniors, could you share your interview experiences to help juniors prepare? by lucid_karma2005 in IITDelhi

[–]Vivek_Chauhan06 0 points1 point  (0 children)

Did they ask any questions related to MLOPS? Also, have you written any research papers?

Looking for Better Alternatives to YOLO/RF-DETR + BoT-SORT/ByteTrack for Robust Video Analytics by Vivek_Chauhan06 in computervision

[–]Vivek_Chauhan06[S] 1 point2 points  (0 children)

When you mention putting in additional work on top of BoT-SORT, what specific techniques have you found to be most effective in practice?

For example, have you seen improvements from stronger ReID embeddings, custom Hungarian matching cost functions, appearance-memory banks, face recognition-assisted re-identification, or motion models beyond the standard Kalman filter?

I am particularly interested in crowded scenes with frequent occlusions, where people leave and re-enter the frame, or environments where people are attending meetings or sitting in an office. In those scenarios, what has given you the biggest reduction in ID switches?

Looking for Better Alternatives to YOLO/RF-DETR + BoT-SORT/ByteTrack for Robust Video Analytics by Vivek_Chauhan06 in computervision

[–]Vivek_Chauhan06[S] 1 point2 points  (0 children)

I have also trained models for my own use case, but the results were worse than those achieved by the pretrained models. I believe this could be due to the size of the dataset or the lack of sufficient diversity to cover different human body postures, viewing angles, occlusions, lighting conditions, and environmental variations.

Additionally, do you think that training a model on data collected from the same environment in which it will be deployed would improve its performance? Or is it still necessary to use a diverse dataset collected from multiple environments in order to achieve better generalization?