In this paper, we present a system framework for event detection in pedestrian and tracking applications. The system is built upon a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes. Upon this framework we propose a pedestrian indexing scheme and suite of tools for detecting events or retrieving data from a given scenario.

Video analysis of crowded scenes is challenging due to the complex motion of individual people in the scene. The collective motion of pedestrians form a crowd flow, but individuals often largely deviate from it as they anticipate and react to each other. Deviations from the crowd decreases the pedestrian’s efficiency: a sociological concept that measures the difference of actual motion from the...

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We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given a collection of normal training examples, e.g., an image sequence or a collection of local spatio-temporal patches, we propose the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. By introducing the prior weight of each basis during sparse rec...

This paper evaluates an automatic technique for detection of abnormal events in crowds. Crowd behaviour is difficult to predict and might not be easily semantically translated. Moreover it is difficult to track individuals in the crowd using state of the art tracking algorithms. Therefore we characterise crowd behaviour by observing the crowd optical flow and use unsupervised feature extraction...

We describe a spatio-temporal feature filtering approach that is appropriate for detecting video events in public scenes containing from many to few people. This non-discrete tracking – or pattern flow analysis – is distinguished by the fact that the usual video processing step of object segmentation is omitted; instead motion features alone are used to detect, follow, and separate activity. Mo...

The task of reliable detection and tracking of multiple objects becomes highly complex for crowded scenarios. Data association is difficult to perform reliably in the presence of missing observations due to occlusions. We propose a novel real-time approach to segment and track multiple overlapping humans. The optimal segmentation solution is given by the maximum likelihood estimate in the joint...

Abnormal crowd behavior has become a popular research topic in recent years. This is related to a rise in the need for electronic video surveillance. Many methods have been proposed to detect abnormalities, but these methods rely on optical flow or classical classification techniques. We propose to follow the general pipeline used by previous works, but upgrade several components with state-of-...

Humans are much more predictable in their transit patterns than we expect. In the presence of su cient observations, it has been shown that our mobility is highly predictable even at a city-scale level [1]. The location of a person at any given time can be predicted with an average accuracy of 93% supposing 3 km of uncertainty. How about at finer resolutions such as in shopping malls, in airpor...

We present a novel approach for continuous detection and tracking of moving objects observed by multiple stationary cameras. We address the tracking problem by simultaneously modeling motion and appearance of the moving objects. The object’s appearance is represented using color distribution model invariant to 2D rigid and scale transformation. It provides an efficient blob similarity measure f...