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Abstract: Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms. In this paper, we integrate appearance information to improve the performance of SORT.

Deep SORT2 is a recent algorithm for tracking that extends Simple Online and Real-time Tracking3 and has shown remarkable results in the Multiple Object Tracking (MOT) problem. In the problem setting of MOT, each frame has more than one object to track.

Essentially, sort uses kalman filter for object tracking without using ego-motion information. The correction information is from associating objects in two adjcent frames.So the Kalman filter can use B's location in frame t+1 as a new measurement for A to update the states.

Deep Sort Algorithm We track based on not just distance, velocity but also what that person looks like. Deep sort allows us to add this feature by computing deep features for every bounding box and using the similarity between deep features to also factor into the tracking logic.

Object tracking is one of the most important tasks in computer vision. It has a multitude of real-life applications, including use cases such as traffic monitoring, robotics, medical imaging, autonomous vehicle tracking, and more.

SORT is a barebones implementation of a visual multiple object tracking framework based on rudimentary data association and state estimation techniques. It is designed for online tracking applications where only past and current frames are available and the method produces object identities on the fly.

NN_BUDGET is a value that indicates how many previous frames of feature vectors should be retained for distance calculation for each track. The max_age parameter of the tracker specifies after how many frames unallocated tracks will be deleted.