Object Tracking by Detection with Visual and Motion Cues

Understand

Self-driving cars and other autonomous vehicles need to detect and track objects in camera images.

• We present a simple online tracking algorithm that is based on a constant velocity motion model with a Kalman filter, and an assignment heuristic.
• The assignment heuristic relies on four metrics: An embedding vector that describes the appearance of objects and can be used to re-identify them, a displacement vector that describes the object movement between two consecutive video frames, the Mahalanobis distance between the Kalman filter states and the new detections, and a class distance.
• These metrics are combined with a linear SVM, and then the assignment problem is solved by the Hungarian algorithm.