Shun Sakai

Video anomaly detection based on locality sensitive hashing filters

In this paper, we propose a novel anomaly detection approach based on Locality Sensitive Hashing Filters (LSHF), which hashes normal activities into multiple feature buckets with Locality Sensitive Hashing (LSH) functions to filter out abnormal activities. An online updating procedure is also introduced into the framework of LSHF for adapting to the changes of the video scenes. Furthermore, we develop a new evaluation function to evaluate the hash map and employ the Particle Swarm Optimization (PSO) method to search for the optimal hash functions, which improves the efficiency and accuracy of the proposed anomaly detection method. Experimental results on multiple datasets demonstrate that the proposed algorithm is capable of localizing various abnormal activities in real world surveillance videos and outperforms state-of-the-art anomaly detection methods. HighlightsWe present a locality sensitive hashing filters based method for anomaly detection.Normal activities are hashed by hash functions into buckets to build filters.Abnormality of a test sample is estimated by filter response of its nearest bucket.Online updating mechanism increase the adaptability to scene changes.Searching for optimal hash functions improves the detection accuracy.Our method performs favorably against previous anomaly detection algorithms.

Boosted pedestrian detector adaptation in specific scenes

In detector adaptation, the quality and quantity of collected online samples are of fundamental importance, yet have not been thoroughly investigated. In this paper, we present an efficient detector adaptation approach with a novel unsupervised online sample collection scheme, which can obtain sufficient aligned samples in a specific video. Unlike other methods that collect samples by only leveraging the detection confidence or track, we select aligned samples by evaluating the alignment scores using a pixel-wise Gaussian Model. Since this selection would lead to an inadequate number of positive samples, we synthesize positive samples by composing the pedestrian foreground in each aligned positive samples with the scene background at different locations. In this way, we can obtain a large number of qualified aligned positive samples encoding new scene information. With sufficient samples, we adopt a simple yet effective method to obtain an adaptive detector, which not only preserves the effective part of the offline boosted detector but also well adapts to the new scene by adding some new trained classifiers. Experiments demonstrate the efficacy of our sample collection scheme and that our approach significantly improves the performance.