We address the problem of detecting and tracking clusters of moving objects in very noisy environments. Monitoring a crowded football stadium for small groups of individuals acting suspiciously is an example instance of this problem. In this example the vast majority of individuals are not part of a suspicious group and are considered as noise. Existing spatio-temporal cluster algorithms are only capable of detecting small clusters in extreme noise when the noise objects are moving randomly. In reality, including the example cited, the noise objects move more systematically instead of moving randomly. The members of the suspicious groups attempt to mimic the behaviors of the crowd in order to blend in and avoid detection. This significantly exacerbates the problem of detecting the true clusters. We propose the use of Support Vector Machines (SVMs) to differentiate the true clusters and their members from the systematically moving noise objects. Our technique utilizes the relational history of the moving objects, implicitly tracked in a relationship graph, and a SVM to increase the accuracy of the clustering algorithm. A modified DBSCAN algorithm is then used to discover clusters of highly related objects from the relationship graph. We evaluate our technique experimentally on several data sets of mobile objects. The experiments show that our technique is able to accurately and efficiently identify groups of suspicious individuals in dense crowds.
Publication: Detecting and Tracking Coordinated Groups in Dense, Systematically Moving, Crowds, James Rosswog and Kanad Ghose, Proc. SIAM Data Mining Conference, 2012, pp. 1-11.