The detection of abnormal behaviours in crowded scenes has to deal with many challenges. This paper presents an efficient method for detection and localization of anomalies in videos. Using fully convolutional neural networks (FCNs) and temporal data, a pre-trained supervised FCN is transferred into an unsupervised FCN ensuring the detection of (global) anomalies in scenes. High performance in terms of speed and accuracy is achieved by investigating the cascaded detection as a result of reducing computation complexities. This FCN-based architecture addresses two main tasks, feature representation and cascaded outlier detection. Experimental results on two benchmarks suggest that detection and localization of the proposed method outperforms existing methods in terms of accuracy.
@cite_47 , an extension of the bag of video words (BOV) approach is used by A context-aware anomaly detection algorithm is proposed in @cite_25 , where the authors represent the video using motions and the context of videos. @cite_57 , a method for modeling both motion and shape with respect to a descriptor (named motion context'') is proposed; they consider anomaly detection as a matching problem. @cite_38 introduce a method for learning the events of a video by using the construction of a hierarchical codebook for dominant events in a video. Ullah et al @cite_18 learn an MLP neural network using trained particles to extract the video behavior. A Gaussian mixture model (GMM) is exploited for learning the behavior of particles using extracted features. In addition, in @cite_4 , an MLP neural network for extracting the corner features from normal training samples is proposed; authors also label the test samples using that MLP.
@cite_38 @cite_18 @cite_4 @cite_57 @cite_47 @cite_25
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In this paper we propose a novel approach to detect anomalies in crowded scenes. This is achieved by analyzing the crowd behavior by extracting the corner features. For each corner feature we collect a set of motion features. The motion features are used to train an MLP neural network during the training stage, and the behavior of crowd is inferred on the test samples. Considering the difficulty of tracking individuals in dense crowds due to multiple occlusions and clutter, in this work we extract corner features and consider them as an approximate representation of the people motion. Corner features are then advected over a temporal window through optical flow tracking. Corner features well match the motion of individuals and their consistency, and accuracy is higher both in structured and unstructured crowded scenes compared to other detectors. In the current work, corner features are exploited to extract motion information, which is used as input prior to train the neural network. The MLP neural network is subsequently used to highlight the dominant corner features that can reveal an anomaly in the crowded scenes. The experimental evaluation is conducted on a set of benchmark video sequences commonly used for crowd motion analysis. In addition, we show that our approach outperforms a state of the art technique proposed in. This paper presents an approach for detecting suspicious events in videos by using only the video itself as the training samples for valid behaviors. These salient events are obtained in real-time by detecting anomalous spatio-temporal regions in a densely sampled video. The method codes a video as a compact set of spatio-temporal volumes, while considering the uncertainty in the codebook construction. The spatio-temporal compositions of video volumes are modeled using a probabilistic framework, which calculates their likelihood of being normal in the video. This approach can be considered as an extension of the Bag of Video words (BOV) approaches, which represent a video as an order-less distribution of video volumes. The proposed method imposes spatial and temporal constraints on the video volumes so that an inference mechanism can estimate the probability density functions of their arrangements. Anomalous events are assumed to be video arrangements with very low frequency of occurrence. The algorithm is very fast and does not employ background subtraction, motion estimation or tracking. It is also robust to spatial and temporal scale changes, as well as some deformations. Experiments were performed on four video datasets of abnormal activities in both crowded and non-crowded scenes and under difficult illumination conditions. The proposed method outperformed all other approaches based on BOV that do not account for contextual information. We present a generic event detection system evaluated in the Surveillance Event Detection (SED) task of TRECVID 2012. We investigate a statistical approach with spatio-temporal features applied to seven event classes, which were defined by the SED task. This approach is based on local spatio-temporal descriptors, called MoSIFT and generated by pair-wise video frames. A Gaussian Mixture Model(GMM) is learned to model the distribution of the low level features. Then for each sliding window, the Fisher vector encoding [improvedFV] is used to generate the sample representation. The model is learnt using a Linear SVM for each event. The main novelty of our system is the introduction of Fisher vector encoding into video event detection. Fisher vector encoding has demonstrated great success in image classification. The key idea is to model the low level visual features as a Gaussian Mixture Model and to generate an intermediate vector representation for bag of features. FV encoding uses higher order statistics in place of histograms in the standard BoW. FV has several good properties: (a) it can naturally separate the video specific information from the noisy local features and (b) we can use a linear model for this representation. We build an efficient implementation for FV encoding which can attain a 10 times speed-up over real-time. We also take advantage of non-trivial object localization techniques to feed into the video event detection, e.g. multi-scale detection and non-maximum suppression. This approach outperformed the results of all other teams submissions in TRECVID SED 2012 on four of the seven event types. Recent progress in the field of human action recognition points towards the use of Spatio-Temporal Interest Points (STIPs) for local descriptor-based recognition strategies. In this paper, we present a novel approach for robust and selective STIP detection, by applying surround suppression combined with local and temporal constraints. This new method is significantly different from existing STIP detection techniques and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-video words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on popular benchmark datasets (KTH and Weizmann), more challenging datasets of complex scenes with background clutter and camera motion (CVC and CMU), movie and YouTube video clips (Hollywood 2 and YouTube), and complex scenes with multiple actors (MSR I and Multi-KTH), validates our approach and show state-of-the-art performance. Due to the unavailability of ground truth action annotation data for the Multi-KTH dataset, we introduce an actor specific spatio-temporal clustering of STIPs to address the problem of automatic action annotation of multiple simultaneous actors. Additionally, we perform cross-data action recognition by training on source datasets (KTH and Weizmann) and testing on completely different and more challenging target datasets (CVC, CMU, MSR I and Multi-KTH). This documents the robustness of our proposed approach in the realistic scenario, using separate training and test datasets, which in general has been a shortcoming in the performance evaluation of human action recognition techniques.