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This is cool, though I wonder how much more accurate this is compared to dlib's built-in Correlation Tracker: http://dlib.net/correlation_tracker.py.html http:/

This is cool, though I wonder how much more accurate this is compared to dlib's built-in Correlation Tracker: http://dlib.net/correlation_tracker.py.html http://dlib.net/correlation_tracker.py.html Problem areas are usually when objects cross over each other, and when the tracked object starts moving at a faster speed (eg: sudden speed up in runners, or so on). *Edit: There's a good gif of dlib's implementation here: https://www.pyimagesearch.com/2018/10/22/object-tracking-with-dlib/ https://www.pyimagesearch.com/2018/10/22/object-tracking-wit...

yeldarb 5y ago
We haven’t had a chance to run it through eval on standard datasets yet to see how it compares quantitatively. I’d like to compare it to some of these: https://paperswithcode.com/task/multi-object-tracking https://paperswithcode.com/task/multi-object-tracking The code is available here if anyone wants to give it a go before we can get to it: https://github.com/roboflow-ai/zero-shot-object-tracking https://github.com/roboflow-ai/zero-shot-object-tracking
nl 5y ago
I believe DLib's tracker is based on tracking hand-made features - probably Cmashift or similar (similar to HOG features on their non-CNN face detector). I'd note that it hasn't been updated sine 2018 as well. That means the accuracy will be a lot lower - although it will be fast. But the Roboflow tracker is using OpenAI Clip, which seems a complete overkill and will be pretty slow! Something using a lightweight object detector should be much faster. This post shows how to use YOLO, which is the first things I'd try would be: https://github.com/yehengchen/Object-Detection-and-Tracking https://github.com/yehengchen/Object-Detection-and-Tracking
jotaf 5y ago
There's also Siamese Networks, they're pretty fast and simple, and people keep updating their backbone CNNs to more modern (computationally expensive) ones.

yeldarb 5y ago

We haven’t had a chance to run it through eval on standard datasets yet to see how it compares quantitatively. I’d like to compare it to some of these: https://paperswithcode.com/task/multi-object-tracking https://paperswithcode.com/task/multi-object-tracking The code is available here if anyone wants to give it a go before we can get to it: https://github.com/roboflow-ai/zero-shot-object-tracking https://github.com/roboflow-ai/zero-shot-object-tracking

nl 5y ago

I believe DLib's tracker is based on tracking hand-made features - probably Cmashift or similar (similar to HOG features on their non-CNN face detector). I'd note that it hasn't been updated sine 2018 as well. That means the accuracy will be a lot lower - although it will be fast. But the Roboflow tracker is using OpenAI Clip, which seems a complete overkill and will be pretty slow! Something using a lightweight object detector should be much faster. This post shows how to use YOLO, which is the first things I'd try would be: https://github.com/yehengchen/Object-Detection-and-Tracking https://github.com/yehengchen/Object-Detection-and-Tracking

jotaf 5y ago

There's also Siamese Networks, they're pretty fast and simple, and people keep updating their backbone CNNs to more modern (computationally expensive) ones.