The paper "Simple Online and Realtime Tracking with a Deep Association Metric" by Nicolai Wojke, Alex Bewley, and Dietrich Paulus introduces an extension to the Simple Online and Realtime Tracking (SORT) algorithm to improve its performance in tracking objects through long periods of occlusions. The authors integrate appearance information by using a pre-trained convolutional neural network (CNN) to compute appearance descriptors, which are then combined with a Mahalanobis distance metric to form a deep association metric. This metric helps in reducing the number of identity switches and improving robustness against misses and occlusions. The method is evaluated on the MOT16 benchmark, showing a 45% reduction in identity switches while maintaining competitive performance in terms of multi-object tracking accuracy (MOTA), track fragmentation, and false negatives. The implementation is efficient, running at approximately 20Hz with modern GPU support.The paper "Simple Online and Realtime Tracking with a Deep Association Metric" by Nicolai Wojke, Alex Bewley, and Dietrich Paulus introduces an extension to the Simple Online and Realtime Tracking (SORT) algorithm to improve its performance in tracking objects through long periods of occlusions. The authors integrate appearance information by using a pre-trained convolutional neural network (CNN) to compute appearance descriptors, which are then combined with a Mahalanobis distance metric to form a deep association metric. This metric helps in reducing the number of identity switches and improving robustness against misses and occlusions. The method is evaluated on the MOT16 benchmark, showing a 45% reduction in identity switches while maintaining competitive performance in terms of multi-object tracking accuracy (MOTA), track fragmentation, and false negatives. The implementation is efficient, running at approximately 20Hz with modern GPU support.