Efficient Multi-Object Tracking by Strong Associations on Temporal Window

Abstract

In this paper, we present a multiple-object vehicle tracking system. We introduce a method to combine bounding boxes extracted from multiple CNNs-based detections as a light and accurate alternative to confidence-based detection methods such as non-maximum suppression and clustering approaches predicting a single bounding box. An intersection over union metric and a threshold value is proposed to determine whether a single detection or a connectivity graph between extracted bounding boxes exists. Affinity measurements are extracted from features representing bounding box geometry, appearance comparison, and changing scene properties. Then, data association is performed by solving the min-cost flow problem of the temporal windows’ affinity network. An affinity network of a directed graph associates the objects and determines whether an existing tracklet is maintained, terminated, or a new tracklet is initiated. Our model is evaluated and tested by KITTI object tracking—Car class benchmark dataset. Overall, the proposed multiple object tracking performance is ranked second according to the multiple object tracking accuracy, mostly tracked, mostly lost statistical metric values assure lower fragmentation and less than half of the captured ID-switch in comparison with respect to the method reaching at the highest multiple object tracking accuracy metric. Furthermore, the runtime is six times faster.