In recent years, there has been an increasing demand for automatic wheelchair-user detection from a surveillance video to support wheelchair users. However, it is difficult to detect them due to occlusions by surrounding pedestrians in a crowded scene. In this paper, we propose a detection method of wheelchair users robust to such occlusions. Concretely, in case the detector cannot a detect whe...

We propose a new algorithm for object tracking in crowded video scenes by exploiting the properties of undecimated wavelet packet transform (UWPT) and interframe texture analysis. The algorithm is initialized by the user through specifying a region around the object of interest at the reference frame. Then, coefficients of the UWPT of the region are used to construct a feature vector (FV) for e...

This paper addresses the problem of human re-identification in videos of dense crowds. Re-identification in crowded scenes is a challenging problem due to large number of people and frequent occlusions, coupled with changes in their appearance due to different properties and exposure of cameras. To solve this problem, we model multiple Personal, Social and Environmental (PSE) constraints on hum...

Recently significant progress has been made in the field of person detection and tracking. However, crowded scenes remain particularly challenging and can deeply affect the results due to overlapping detections and dynamic occlusions. In this paper, we present a method to enhance human detection and tracking in crowded scenes. It is based on introducing additional information about crowds and i...

We propose a novel unsupervised learning framework to model activities and interactions in crowded and complicated scenes. Under our framework, hierarchical Bayesian models are used to connect three elements in visual surveillance: low-level visual features, simple “atomic” activities, and interactions. Atomic activities are modeled as distributions over low-level visual features, and multiagen...

Due to the increasing number of violence cases, there is a high demand for efficient monitoring systems, however, these systems can be susceptible failure. Therefore, this work proposes analysis and application low-cost Convolutional Neural Networks (CNNs) techniques automatically recognize classify suspicious events. Thus, it possible alert assist process with reduced deployment cost. For purp...

Animation, especially when involving human figures is both a labour-intensive and time-consuming task. Currently animators, using dedicated software can decrease both factors by applying a keyframing approach (the user specifies key frames and the software interpolates between them) but the process is still tedious and expensive. Those problems become especially apparent in the special effects ...

This paper presents a novel approach to learning a dictionary of crowd prototypes for dynamic visual scenes. Recent work in cognitive psychology suggests that crowd perception may be based on pre-attentive ensemble coding mechanisms [24] in the spirit of feedforward hierarchical models of visual processing [4]. We extend a biological model of motion processing [10] with a new dictionary learnin...

This paper addresses the problem of detecting coherent motions in crowd scenes and subsequently constructing semantic regions for activity recognition. We first introduce a coarse-to-fine thermal-diffusionbased approach. It processes input motion fields (e.g., optical flow fields) and produces a coherent motion filed, named as thermal energy field. The thermal energy field is able to capture bo...