In this paper, we present our eSur (Event detection system on SURveillance video) system, which is derived from TRECVID'09 surveillance tasks. Currently, eSur attempts to detect two categories of events: 1) single-actor events (i.e., PersonRuns and ElevatorNoEntry) irrespective of any interaction between individuals, and 2) pair-activity events (i.e., PeopleMeet, PeopleSplitUp, and Embrace) involves more than one individual. eSur consists of three major stages, i.e., preprocessing, event classification, and post-processing. The preprocessing involves view classification, background subtraction, head-shoulder detection, human body detection and object tracking. Event classification fuses One-vs.-All SVM and rule-based classifiers to identify single-actor and pair-activity events in an ensemble way. To reduce false alarms, we introduce prior knowledge into the post-processing, and in particular, we apply a so-called event merging process over TRECVID dataset. Extensive experiments have been performed over TRECVid'08 and '09 ED data corpus involving in total 144 hours surveillance video of London Gatwick airport. According to the TRECVid-ED formal evaluation, our prototype has yielded fairly promising results over TRECVid'09 dataset, with top Act.DCR of 1.023, 1.025, 1.02, and 0.334 for PeopleMeet, PeopleSplitUp, Embrace, and ElevatorNoEntry, respectively.

Multi-cue integration has proved successful at increasing the robustness of tracking algorithms and overcoming the failure cases of individual cue. But considering dynamic appearance of objects or clutter background, the integration based on constant weights may weaken the performance of this scheme. In this paper, we propose a dynamic weights update mechanism for multiple cues tracking with detection responses as supervision. We integrate multiple cues based on the observation hypotheses compared with detection association results and adjust the weights according to the approximation degree. The integration is adapted on-the-fly during tracking, in order to keep the tracker adaptive. The proposed method allows flexible combination of different cues and we select cues based on color and local feature for tracking. Experiments are carried out on 602 trajectories extracted from TRECVID 2008 event detection dataset which is recorded in an airport scenario. Comparison results prove the effectiveness of our method.

Visual saliency can be a useful tool for image content analysis such as automatic image cropping and image compression. In existing methods on visual saliency detection, most of them are related to the model of receptive field. In this paper, we propose a bottom-up model which introduces 2D Log-Gabor wavelets for saliency detection. Compared with the traditional model of receptive field, the 2D Log-Gabor wavelets can better simulate the biological characteristics of the simple cortical cell in the receptive filed. Moreover, we also incorporate the influence of center bias into our model, which is a common phenomenon that directs visual attention to the center of images in natural scenes. Experimental results show that our approach outperforms three state-of-the-art approaches remarkably.