Crowd Saliency Detection via Global Similarity Structure

It is common for CCTV operators to overlook inter- esting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveillance. This paper proposes a novel framework to identify and localize salient regions in a crowd scene, by transforming low-level features extracted from crowd motion field into a global similarity structure. The global similarity structure representation allows the discovery of the intrinsic manifold of the motion dynamics, which could not be captured by the low-level representation. Ranking is then performed on the global similarity structure to identify a set of extrema. The proposed approach is unsupervised so learning stage is eliminated. Experimental results on public datasets demonstrates the effectiveness of exploiting such extrema in identifying salient regions in various crowd scenarios that exhibit crowding, local irregular motion, and unique motion areas such as sources and sinks.Comment: Accepted in ICPR 2014 (Oral). Mei Kuan Lim and Ven Jyn Kok share equal contribution

Contour Based Tracking for Driveway Entrance Counting System

Managing vehicle in free-flow entrance is tiring to do manually by a guard control especially due to the increase in transportation demand. Providing an accurate vehicle counting approach is vital for traffic management and it will surely be an essential part in tomorrow's smart cities. Therefore, the main objective of this paper is to propose a more accurate vehicle counter by using the tracking and heuristic rules approaches. EzCam v1.0 is a vehicle surveillance system for a free-flow entrance where a module of vehicle counting based on proposed idea has been applied. The proposed method does not require high computational resources more than any relatively affordable non task specific hardware. It employs single threshold, contour extraction and sequential frame analysis and finally, vehicle counting process subsequently. The tracking-based method employs foreground object detection method and a mechanism for object filtering approach as compared to Chris Dahms approach which does not consider any object rejection and accept all contour information as relevant to be counted as vehicles. As a result, EzCam v1.0 which utilizes the exploited contour-based approach is able to achieve up to 94 percent of accuracy rate and outperforms the classic Chris Dahms method which obtained an accuracy of 88 percent. Therefore, the Exploited Contour based tracking method helps vehicle counting system to perform better accuracy in comparison to Chris Dahms approach

Visual analysis of dense crowds / Kok Ven Jyn

The steady worldwide population growth with continuing urbanization renders the formation of crowd by chance a norm. The mere existence of crowd has the prospect of progressing into a hazardous scene. Consequently, visual analysis of dense crowds is a growing research topic in the domain of computer vision. Conventional visual analysis methods are mostly object-centric, thus, are neither suitable nor capable of analyzing dense crowd. Hence, this thesis proposes novel solutions to analyze images and videos of dense crowds, which contain hundreds to thousands of individuals. The main objective are, first, to obviate the difficulty of segregating individuals in dense crowd scenes to infer dense crowd segments, secondly to estimate the number of individuals and finally to detect unusual events, by exploiting spatial and temporal cues readily available from the scenes. Dense crowd segmentation generally serves as one of the essential steps for further visual analysis of the dense crowds. The thesis first demonstrates the significance of simplifying dense crowd scenes into structurally meaningful atomic regions for dense crowd segmentation. This proposed approach is formulated using the concept and principles of granular computing. It shows that by exploiting the correlation among pixel granules, structurally similar pixels can be aggregated into meaningful atomic structure granules. This is useful in outlining natural boundaries between crowd and background (i.e. non-crowd) regions necessary for dense crowd segmentation. Moreover, the proposed approach is scene-independent; thus it can be applied effectively to dense crowd scenes with a variety of physical layout and crowdedness. Second, this thesis presents an approach to utilize irregular patches conforming to the natural outline between crowd and background to estimate the number of individuals in dense crowd scenes. As opposed to most of the existing approaches that uses pixel-grid representation, the proposed density estimation approach allows a model to adapt itself to the arbitrary distribution of crowd where the underlying spatial information of scenes can be accurately extracted. Here, a direct mapping is established between the extracted features and the number of people. Third, to detect saliency in dense crowd scenes, low-level features extracted from the crowd motion field are transformed into a global similarity structure. This global similarity structure representation allows the discovery of the intrinsic manifold of the motion dynamics, which could not be captured by the low-level representation. Most importantly, unlike conventional methods, the proposed approach does not require tracking, and prior information or model learning to identify interesting / salient regions in the dense crowd scenes. These proposed approaches are validated by using public dataset of dense crowd scenes. From the empirical results, it is anticipated that the collective analysis of this thesis will constitute a complete dense crowd analysis system that is able to infer regions of dense crowds, estimate crowd density and identify saliency in mass gathering for proactive crowd management

Granular-based dense crowd density estimation

Dense crowd density estimation is one of the fundamental tasks in crowd analysis. While tremendous progress has been made to understand crowd scenes along with the rise of Convolutional Neural Networks (CNNs), research work on dense crowd density estimation is still an ongoing process. In this paper, we propose a novel approach to learn discriminative crowd features from granules, that conforms to the outline between crowd and background (i.e. non-crowd) regions, for density estimation. It shows that by studying the inner statistics of granules for density estimation, this approach is adaptive to arbitrary distribution of crowd (i.e. scene independent). Multiple features fusion is proposed to learn discriminative crowd features from granules. This is to be used as description of the crowd where a direct mapping between the features and crowd density is learned. Extensive experiments on public benchmark datasets demonstrate the effectiveness of our novel approach for scene independent dense crowd density estimation