The SmarTrack Project at FBK: Past and Ongoing Efforts on People Tracking for Surveillance and Monitoring

Progress in computer vision research is reshaping the video surveillance sector: high-tech companies are starting to offer CCTV based systems now empowered with Video Analytics, i.e. with software solutions able to generate meaningful alerts by analyzing video feeds. Most surveillance applications target people as their subject of study, where they move, how they behave, and what they carry (or leave unattended); people tracking is therefore becoming a ever more important functionality for new generation technology. In this document we summarize our efforts in realizing SmarTrack, a multi camera people tracker developed by FBK over the last few years. We detail main research results, development efforts and applications, and present current and future research directions

A sampling algorithm for occlusion robust multi target detection

Bayesian methods for visual tracking, with the particle filter as its most prominent instance, have proven to work effectively in the presence of clutter, occlusions, and dynamic background. When applied to track a variable number of targets, however, they become inefficient due to the absence of strong priors. In this paper we present an efficient sampling algorithm for target detection build upon an informed prior that is derived as the inverse of an occlusion robust image likelihood. It has the advantage of being fully integrated in the Bayesian tracking framework, and reactive as it uses sparse features not explained by tracked objects