imp-deepsort

DeepSORT is a state-of-the-art object tracking algorithm that utilizes deep learning techniques for improved tracking accuracy and robustness. It is an extension of the popular SORT (Simple Online and Realtime Tracking) algorithm, which is widely used for multi-object tracking in various applications.

The main advantage of DeepSORT is that it uses deep neural networks to learn appearance features of objects, which are then used to associate object detections across frames. This allows DeepSORT to handle occlusions, track objects over longer periods of time, and reduce false positive and false negative detections.

DeepSORT consists of two main components: a detection model and a tracking model. The detection model is responsible for detecting objects in each frame, while the tracking model associates detections across frames and maintains a track of each object. The tracking model uses an extended Kalman filter to predict the position and velocity of each object, and a Hungarian algorithm to match detections across frames.

Overall, DeepSORT is a powerful and flexible object tracking algorithm that can be applied to a wide range of applications, including surveillance, autonomous driving, and robotics.