Video anomaly detection and localization by local motion based joint video representation and OCELM

Nowadays, human-based video analysis becomes increasingly exhausting due to the ubiquitous use of surveillance cameras and explosive growth of video data. This paper proposes a novel approach to detect and localize video anomalies automatically. For video feature extraction, video volumes are jointly represented by two novel local motion based video descriptors, SL-HOF and ULGP-OF. SL-HOF descriptor captures the spatial distribution information of 3D local regions’ motion in the spatio-temporal cuboid extracted from video, which can implicitly reflect the structural information of foreground and depict foreground motion more precisely than the normal HOF descriptor. To locate the video foreground more accurately, we propose a new Robust PCA based foreground localization scheme. ULGP-OF descriptor, which seamlessly combines the classic 2D texture descriptor LGP and optical flow, is proposed to describe the motion statistics of local region texture in the areas located by the foreground localization scheme. Both SL-HOF and ULGP-OF are shown to be more discriminative than existing video descriptors in anomaly detection. To model features of normal video events, we introduce the newly-emergent one-class Extreme Learning Machine (OCELM) as the data description algorithm. With a tremendous reduction in training time, OCELM can yield comparable or better performance than existing algorithms like the classic OCSVM, which makes our approach easier for model updating and more applicable to fast learning from the rapidly generated surveillance data. The proposed approach is tested on UCSD ped1, ped2 and UMN datasets, and experimental results show that our approach can achieve state-of-the-art results in both video anomaly detection and localization task.This work was supported by the National Natural Science Foundation of China (Project nos. 60970034, 61170287, 61232016)

Identification of undesirable behaviour in CCTV footage using Deep Learning

Anomaly detection in CCTV recording is a difficult and challenging subject due to the issue of the vast amount of the data that must be processed, and the expertise required to analyse it. CCTV operators undergo a long and extensive training to spot anomalous behaviour in CCTV recording, but even with the acquired expertise, on average an operator will lose up to 45% of screen activities after 12 minutes, and up to 95% after 22 minutes. This research investigates a novel pipeline technique to process CCTV recording using a combination of different unsupervised machine learning techniques. The principle pipeline technique evaluated consists of and Autoencoder as a feature extractor, in combination with a one-class Support Vector Machine (SVM), and Hidden Markov Model (HMM). Extracted Autoencoder features are categorised using the SVM to determine anomaly per frame, followed by temporal smoothing of the SVM frame categorisation with the HMM. The system achieves an accuracy of 61.38% and an AUC of 0.59. The system was evaluated by comparing the results produced by the system with regards to labels provided with a dataset. The results collected from the comparison were used to produce an area under curve value. The report will look in to comparing the results of using a pre-trained CNN (VGG16) and Autoencoder for purpose of feature extraction. Being unsupervised, the system requires very little human interference and it was designed to teach itself how to differentiate an anomaly from a non-anomalous event. The only human input that the system requires was the selection of parameters for all the algorithms. The rest was left for algorithm to decide based on a set criterion. The obtained results, which while inevitably inferior to the performance of comparable supervised systems (i.e. where the anomaly class is explicitly labelled in the training data), provides an effective proof of concept of pipelining that can be used for purpose of unsupervised anomaly detection of a CCTV image frame