Abstract
We propose a novel Gaussian kernel based integration model (GKIM) for anomalous entities detection and localization in pedestrian flows. The GKIM integrates spatio-temporal features for efficient and robust motion representation to capture the distinctive and meaningful information about the anomalous entities. We next propose a block based detection framework by training a recurrent conditional random field (R-CRF) using the GKIM features. The trained R-CRF model is then used to detect and localize the anomalous entities during the online testing stage. We conduct comprehensive experiments on three benchmark datasets and compare the performance of the proposed method with the state-of-the-art anomalous entities detection methods. Our experiments show that the proposed GKIM outperforms the compared methods in terms of equal error rate (EER) and detection rate (DR) in both frame-level and pixel-level comparisons. The frame-level analysis detects the presence of an anomalous entity in a frame regardless of its location. The pixel-level analysis localizes the anomalous entity in term of its pixels.