Electrical and Electronic Engineering - Research Publications

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Start date: 2010 × Author: Palaniswami, Marimuthu × Author: Leckie, Christopher × Author: Rashidi, Lida × Type: Conference Paper ×

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    Cluster-based Crowd Movement Behavior Detection
    Yang, M ; Rashidi, L ; Rao, AS ; Rajasegarar, S ; Ganji, M ; Palaniswami, M ; Leckie, C ; Murshed, M ; Paul, M ; Asikuzzaman, M ; Pickering, M ; Natu, A ; RoblesKelly, A ; You, S ; Zheng, L ; Rahman, A (IEEE, 2019-01-01)
    Crowd behaviour monitoring and prediction is an important research topic in video surveillance that has gained increasing attention. In this paper, we propose a novel architecture for crowd event detection, which comprises methods for object detection, clustering of various groups of objects, characterizing the movement patterns of the various groups of objects, detecting group events, and finding the change point of group events. In our proposed framework, we use clusters to represent the groups of objects/people present in the scene. We then extract the movement patterns of the various groups of objects over the video sequence to detect movement patterns. We define several crowd events and propose a methodology to detect the change point of the group events over time. We evaluated our scheme using six video sequences from benchmark datasets, which include events such as walking, running, global merging, local merging, global splitting and local splitting. We compared our scheme with state of the art methods and showed the superiority of our method in accurately detecting the crowd behavioral changes.
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    Crowd Activity Change Point Detection in Videos via Graph Stream Mining
    Yang, M ; Rashidi, L ; Rajasegarar, S ; Leckie, C ; Rao, AS ; Palaniswami, M (IEEE, 2018)
    In recent years, there has been a growing interest in detecting anomalous behavioral patterns in video. In this work, we address this task by proposing a novel activity change point detection method to identify crowd movement anomalies for video surveillance. In our proposed novel framework, a hyperspherical clustering algorithm is utilized for the automatic identification of interesting regions, then the density of pedestrian flows between every pair of interesting regions over consecutive time intervals is monitored and represented as a sequence of adjacency matrices where the direction and density of flows are captured through a directed graph. Finally, we use graph edit distance as well as a cumulative sum test to detect change points in the graph sequence. We conduct experiments on four real-world video datasets: Dublin, New Orleans, Abbey Road and MCG Datasets. We observe that our proposed approach achieves a high F-measure, i.e., in the range [0.7, 1], for these datasets. The evaluation reveals that our proposed method can successfully detect the change points in all datasets at both global and local levels. Our results also demonstrate the efficiency and effectiveness of our proposed algorithm for change point detection and segmentation tasks.