A Real-Time Processing Stand-Alone Multiple Object Visual Tracking System

Detection and tracking of multiple objects in real applications requires real-time performance, the management of tens of simultaneous objects, and handling frequent partial and total occlusions. More- over, due to the software and hardware requirements of the different algorithms, this kind of systems require a distributed architecture to run in real-time. In this paper, we propose a vision based tracking system with three components: detection, tracking and data association. Tracking is based on a Discriminative Correlation Filter combined with a Kalman filter for occlusions handling. Also, our data association uses deep features to improve robustness. The complete system runs in real- time with tens of simultaneous objects, taking into account the runtimes of the Convolutional Neural Network detector, the tracking and the data association.

keywords: Multiple object tracking, Convolutional Neural Network, Data association.