Detecting abnormal events in video using Narrowed Normality Clusters

We formulate the abnormal event detection problem as an outlier detection task and we propose a two-stage algorithm based on k-means clustering and one-class Support Vector Machines (SVM) to eliminate outliers. In the feature extraction stage, we propose to augment spatio-temporal cubes with deep appearance features extracted from the last convolutional layer of a pre-trained neural network. After extracting motion and appearance features from the training video containing only normal events, we apply k-means clustering to find clusters representing different types of normal motion and appearance features. In the first stage, we consider that clusters with fewer samples (with respect to a given threshold) contain mostly outliers, and we eliminate these clusters altogether. In the second stage, we shrink the borders of the remaining clusters by training a one-class SVM model on each cluster. To detected abnormal events in the test video, we analyze each test sample and consider its maximum normality score provided by the trained one-class SVM models, based on the intuition that a test sample can belong to only one cluster of normality. If the test sample does not fit well in any narrowed normality cluster, then it is labeled as abnormal. We compare our method with several state-of-the-art methods on three benchmark data sets. The empirical results indicate that our abnormal event detection framework can achieve better results in most cases, while processing the test video in real-time at 24 frames per second on a single CPU.Comment: Accepted at WACV 2019. arXiv admin note: text overlap with arXiv:1705.0818

Two-stage sparse representation based abnormal crowd event detection in videos

Ubiquitous surveillance has become part of our lives to increase security and safety. Despite the wide application of surveillance systems, their efficiency is limited by human factors, such as boredom and fatigue; because most of the time, nothing unusual happens. In safety-critical applications, time is essential and it is vital to act fast to prevent costly incidents. This thesis proposes a two-stage abnormal crowd event detection framework based on k-means clustering in the first stage, and sparse representation based methods in the second stage, to alleviate the laborious task of video monitoring. We conduct a literature review of 18 studies, where we specifically focus on sparse representation based methods. Accordingly, we choose the spatio-temporal gradient feature due to its simplicity, efficiency, and effectiveness in motion representation. After extracting features only from normal events, k-means clustering is applied to separate different motion feature clusters. Then, clusters with smaller samples, which are deemed to contain mostly abnormal features, are removed according to a threshold. In the second stage, we learn a dictionary for each remaining cluster using the approximate K-SVD algorithm. In testing, the reconstruction error of a feature against a learned dictionary and its sparse representation is used to determine an abnormality. We conduct extensive experiments on a standard dataset to evaluate the detection performance of the method. Furthermore, the effect of hyper-parameters in our method is investigated. We also compare our method with different methods to examine its effectiveness. Results indicate that our abnormal event detection framework can successfully understand abnormal events in a scene while running in real-time at 161 frames per second. With a few exceptions, no significant advantage of the two-stage sparse representation approach over a single large dictionary was found. We speculate that these results may be influenced by a small sample size. Nevertheless, our approach, due to its unsupervised nature, can be adapted to different contexts without additional annotation effort and using only normal events from videos. Therefore it motivates us for further development

Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks

Detection of anomalous trajectories is an important problem with potential applications to various domains, such as video surveillance, risk assessment, vessel monitoring and high-energy physics. Modeling the distribution of trajectories with statistical approaches has been a challenging task due to the fact that such time series are usually non stationary and highly dimensional. However, modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction. In this paper, we propose a Sequence to Sequence architecture for real-time detection of anomalies in human trajectories, in the context of risk-based security. Our detection scheme is tested on a synthetic dataset of diverse and realistic trajectories generated by the ISL iCrowd simulator. The experimental results indicate that our scheme accurately detects motion patterns that deviate from normal behaviors and is promising for future real-world applications.Comment: AVSS 201

A Scene-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video

Abnormal event detection in video is a complex computer vision problem that has attracted significant attention in recent years. The complexity of the task arises from the commonly-agreed definition of an abnormal event, that is, a rarely occurring event that typically depends on the surrounding context. Following the standard formulation of abnormal event detection as outlier detection, we propose a scene-agnostic framework that learns from training videos containing only normal events. Our framework is composed of an object detector, a set of appearance and motion auto-encoders, and a discriminator. Since our framework only looks at object detections, it can be applied to different scenes, provided that abnormal events are defined identically across scenes. This makes our method scene agnostic, as we rely strictly on objects that can cause anomalies, and not on the background. To overcome the lack of abnormal data during training, we propose an adversarial learning strategy for the auto-encoders. We create a scene-agnostic set of out-of-domain adversarial examples, which are correctly reconstructed by the auto-encoders before applying gradient ascent on the adversarial examples. We further utilize the adversarial examples to serve as abnormal examples when training a binary classifier to discriminate between normal and abnormal latent features and reconstructions. Furthermore, to ensure that the auto-encoders focus only on the main object inside each bounding box image, we introduce a branch that learns to segment the main object. We compare our framework with the state-of-the-art methods on three benchmark data sets, using various evaluation metrics. Compared to existing methods, the empirical results indicate that our approach achieves favorable performance on all data sets.Comment: Under revie

Detection and Simulation of Dangerous Human Crowd Behavior

Tragically, gatherings of large human crowds quite often end in crowd disasters such as the recent catastrophe at the Loveparade 2010. In the past, research on pedestrian and crowd dynamics focused on simulation of pedestrian motion. As of yet, however, there does not exist any automatic system which can detect hazardous situations in crowds, thus helping to prevent these tragic incidents. In the thesis at hand, we analyze pedestrian behavior in large crowds and observe characteristic motion patterns. Based on our findings, we present a computer vision system that detects unusual events and critical situations from video streams and thus alarms security personnel in order to take necessary actions. We evaluate the system’s performance on synthetic, experimental as well as on real-world data. In particular, we show its effectiveness on the surveillance videos recorded at the Loveparade crowd stampede. Since our method is based on optical flow computations, it meets two crucial prerequisites in video surveillance: Firstly, it works in real-time and, secondly, the privacy of the people being monitored is preserved. In addition to that, we integrate the observed motion patterns into models for simulating pedestrian motion and show that the proposed simulation model produces realistic trajectories. We employ this model to simulate large human crowds and use techniques from computer graphics to render synthetic videos for further evaluation of our automatic video surveillance system