Mei Kuan Lim

Crowd behavior analysis

Although the traits emerged in a mass gathering are often non-deliberative, the act of mass impulse may lead to irrevocable crowd disasters. The two-fold increase of carnage in crowd since the past two decades has spurred significant advances in the field of computer vision, towards effective and proactive crowd surveillance. Computer vision studies related to crowd are observed to resonate with the understanding of the emergent behavior in physics (complex systems) and biology (animal swarm). These studies, which are inspired by biology and physics, share surprisingly common insights, and interesting contradictions. However, this aspect of discussion has not been fully explored. Therefore, this survey provides the readers with a review of the state-of-the-art methods in crowd behavior analysis from the physics and biologically inspired perspectives. We provide insights and comprehensive discussions for a broader understanding of the underlying prospect of blending physics and biology studies in computer vision. HighlightsReview crowd behavior studies in computer vision from physics and biology outlooks.Overview of the key attributes of crowd from the perspectives of the two sciences.General attributes of crowd: decentralized, collective motion, emergent behavior.Contradicting attributes of crowd: thinking/non-thinking, bias/non-bias.Discuss sample applications of crowd based on attributes and benchmarked datasets.

iSurveillance: Intelligent framework for multiple events detection in surveillance videos

Abstract Research in the video surveillance is gaining more popularity due to its widespread applications as well as social impact. In this paper, we present an intelligent framework for detection of multiple events in surveillance videos. Based on the principle of compositionality, we modularize the surveillance problems into a set of variables comprising regions-of-interest, classes (i.e. human, vehicle), attributes (i.e. speed, locality) and a set of notions (i.e. rules) associated to each of the attributes to construct a knowledge-based understanding of the environment. The final output from the reasoning process, which combines the definition domains of the various variables, allows a broader and integrated understanding of complex pattern of activities in the scene. This is in contrast to the state-of-the-art solutions that are only able to perform only a singular task, at a time. Experimental results on both the public and real-time datasets have demonstrated the effectiveness and robustness of the proposed framework in detecting multiple events in surveillance videos.

Crowd Saliency Detection via Global Similarity Structure

It is common for CCTV operators to overlook interesting 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 extrem a. The proposed approach is unsupervised so learning stage is eliminated. Experimental results on public datasets demonstrates the effectiveness of exploiting such extrem a in identifying salient regions in various crowd scenarios that exhibit crowding, local irregular motion, and unique motion areas such as sources and sinks.

Hybrid blob and particle filter tracking approach for robust object tracking

Abstract Analysing and characterising human behaviour is now receiving much attention from the visual surveillance research community. Generally, human behaviour recognition requires human to be detected and tracked so that the trajectory patterns of the human can be captured and analysed for further interpretation. Therefore, it is crucial for tracking algorithms to be fast and robust to partial and short-life occlusion. In addition, the detection of object-of-interest to be tracked should be automated, without the need for manual intervention. This paper thus proposes a tracking system targeted for real time surveillance applications that integrate blob and simplified particle filter tracking approaches so as to exploit the advantages of both approaches while minimizing their respective disadvantages. The blob approach acts as the main tracking and will invoke the simplified particle filter tracking in the event of blob merging or occlusion. In this paper, the proposed tracking method is tested using PETS 2009 sequences to illustrate the capability of solving occlusion and obstruction in the scene. The results show that the proposed system successfully tracks objects during and after occlusion with other objects or after obstructed by the background.

Refined particle swarm intelligence method for abrupt motion tracking

Conventional tracking solutions are not able to deal with abrupt motion as these are based on a smooth motion assumption or an accurate motion model. Abrupt motion is not subject to motion continuity and smoothness. We address this problem by casting tracking as an optimisation problem and propose a novel abrupt motion tracker based on swarm intelligence – the SwATrack. Unlike existing swarm-based filtering methods, we first of all introduce an optimised swarm-based sampling strategy for a tradeoff between the exploration and exploitation of the state space in search for the optimal proposal distribution. Secondly, we propose Dynamic Acceleration Parameters (DAP) that allow on the fly tuning of the best mean and variance of the distribution for sampling. Combining the two strategies within the Particle Swarm Optimisation framework represents a novel method to address abrupt motion. To the best of our knowledge, this has never been done before. Thirdly, we introduce a new dataset – the Malaya Abrupt Motion (MAMo) dataset that consists of 12 videos with groundtruth. Finally, experimental on both quantitative and qualitative results have shown the effectiveness of the proposed method in terms of dataset unbiased, object size invariant and fast recovery in tracking the abrupt motions.

Investigations into Particle Swarm Optimization for Multi-class Shape Recognition

There has been a significant drop in the cost as well as an increase in the quality of imaging sensors due to stiff competition as well as production improvements. Consequently, real-time surveillance of private or public spaces which relies on such equipment is gaining wider acceptance. While the human brain is very good at image analysis, fatigue and boredom may contribute to a less-than-optimum level of monitoring performance. Clearly, it would be good if highly accurate vision systems could complement the role of humans in round-the-clock video surveillance. This paper addresses an image analysis problem for video surveillance based on the particle swarm computing paradigm. In this study three separate datasets were used. The overall finding of the paper suggests that clustering using Particle Swarm Optimization leads to better and more consistent results, in terms of both cluster characteristics and subsequent recognition, as compared to traditional techniques such as K-Means.

Hybrid particle swarm optimisation for data clustering

Finding a best clustering algorithm to tackle the problem of finding the optimal partition of a data set is always an NP-hard problem. In general, solutions to the NP-hard problems involve searches through vast spaces of possible solutions and evolutionary algorithms have been a success. In this paper, we explore one such approach which is hardly known outside the search heuristic field - the Particle Swarm Optimisation+k-means (PSOk) for this purpose. The proposed hybrid algorithm consists of two modules, the PSO module and the k-means module. For the initial stage, the PSO module is executed for a short period to search for the clusters centroid locations. Succeeding to the PSO module is the refining stage where the detected locations are transferred to the k-means module for refinement and generation of the final optimal clustering solution. Experimental results on two challenging datasets and a comparison with other hybrid PSO methods has demonstrated and validated the effectiveness of the proposed solution in terms of precision and computational complexity.