Crowd detection and counting using a static and dynamic platform: state of the art

Automated object detection and crowd density estimation are popular and important area in visual surveillance research. The last decades witnessed many significant research in this field however, it is still a challenging problem for automatic visual surveillance. The ever increase in research of the field of crowd dynamics and crowd motion necessitates a detailed and updated survey of different techniques and trends in this field. This paper presents a survey on crowd detection and crowd density estimation from moving platform and surveys the different methods employed for this purpose. This review category and delineates several detections and counting estimation methods that have been applied for the examination of scenes from static and moving platforms

MULTI-TARGET TRACKING BY DISCRIMINATIVE ANALYSIS ON RIEMANNIAN MANIFOLD

This paper addresses the problem of multi-target tracking in crowded scenes from a single camera. We propose an algorithm for learning discriminative appearance models for different targets. These appearance models are based on covariance descriptor extracted from tracklets given by a short-term tracking algorithm. Short-term tracking relies on object descriptors tuned by a controller which copes with context variation over time. We link tracklets by using discriminative analysis on a Riemannian manifold. Our evaluation shows that by applying this discriminative analysis, we can reduce false alarms and identity switches, not only for tracking in a single camera but also for matching object appearances between non-overlapping cameras. Index Terms — tracking, controller, re-identification, covariance matri