In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. Pedestrian tracking is achieved ...

The performance of conventional surveillance systems is challenged by high error detection rates in busy scenes, which has significantly affected the accurate current system. Feature representation and object pattern extraction from different scenes have made deep learning (DL) promising methods systems, compared to approaches where features are created manually. To improve accuracy, this paper...

Occlusion and lack of visibility in dense crowded scenes make it very difficult to track individual people correctly and consistently. This problem is particularly hard to tackle in single camera systems. We present a multi-view approach to tracking people in crowded scenes where people may be partially or completely occluding each other. Our approach is to use multiple views in synergy so that...

In this paper, we propose an accurate and real-time anomaly detection and localization in crowded scenes, and two descriptors for representing anomalous behavior in video are proposed. We consider a video as being a set of cubic patches. Based on the low likelihood of an anomaly occurrence, and the redundancy of structures in normal patches in videos, two (global and local) views are considered...

Two key questions concerning change detection in crowded acoustic environments are the extent to which cortical processing is specialized for different forms of acoustic change and when in the time-course of cortical processing neural activity becomes predictive of behavioral outcomes. Here, we address these issues by using magnetoencephalography (MEG) to probe the cortical dynamics of change d...

Based on the Lagrangian framework for fluid dynamics, a streakline representation of flow is presented to solve computer vision problems involving crowd and traffic flow. Streaklines are traced in a fluid flow by injecting color material, such as smoke or dye, which is transported with the flow and used for visualization. In the context of computer vision, streaklines may be used in a similar w...