Smat: Smart multiple affinity metrics for multiple object tracking

Abstract

This research introduces a novel multiple object tracking algorithm called SMAT (Smart Multiple Affinity Metric Tracking) that works as an online tracking-by-detection approach. The use of various characteristics from observation is established as a critical factor for improving tracking performance. By using the position, motion, appearance, and a correction component, our approach achieves an accuracy comparable to state of the art trackers. We use the optical flow to track the motion of the objects, we show that tracking accuracy can be improved by using a neural network to select key points to be tracked by the optical flow. The proposed algorithm is evaluated by using the KITTI Tracking Benchmark for the class CAR.