Enhance Multi-Object Tracking with FairMOT: Accurate Re-Identification 🚀

FairMOT is a model for multi-object tracking which consists of two homogeneous branches to predict pixel-wise objectness scores and re-ID features.
Arguably, the most crucial task of a Deep Learning based Multiple Object Tracking (MOT) is not to identify an object, but to re-identify it after occlusion. There are a plethora of trackers available to use, but not all of them have a good re-identification pipeline. Here we will focus on one such tracker, FairMOT, that revolutionized the joint optimization of detection and re-identification tasks in tracking.

We will learn about the following:

✅About MOTs
✅Problems faced because of previous trackers
✅The problems FairMOT tackles
✅FairMOT’s homogenous architecture
✅The detection branch and its various heads
✅Re-ID branch and the embeddings
✅Association stage of FairMOT
✅The results on public datasets
✅Comparison with DeepSORT

Here we understand and explain the inner workings of FairMOT Tracker. Checkout the intermediate outputs, and compare the results with DeepSort Tracker

📚 Blog post link: https://learnopencv.com/object-tracking-and-reidentification-with-fairmot/

🖥️ On our blog - https://learnopencv.com we also share tutorials and code on topics like Image Processing, Image Classification, Object Detection, Face Detection, Face Recognition, YOLO, Segmentation, Pose Estimation, and many more using OpenCV(Python/C++), PyTorch, and TensorFlow.

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⭐️ Time Stamps:⭐️
0:00-00:10: Introduction
00:10-00:33: Object Tracking
00:33-00:45: Approaches to Tracking & Re-ID
00:45-03:09: FairMOT
03:09-03:22: DeepSort Vs FairMOT Results

🔖Hashtags🔖
#AI #fairmot #fairmotarchitecture #machinelearning #objectdetection #deeplearning #computervision #objecttracking