Pattern Anomaly Detection based on Sequence-to-Sequence Regularity Learning

Anomaly detection in traffic surveillance videos is a challenging task due to the ambiguity of anomaly definition and the complexity of scenes. In this paper, we propose to detect anomalous trajectories for vehicle behavior analysis via learning regularities in data. First, we train a sequence-to-sequence model under the autoencoder architecture and propose a new reconstruction error function for model optimization and anomaly evaluation. As such, the model is forced to learn the regular trajectory patterns in an unsupervised manner. Then, at the inference stage, we use the learned model to encode the test trajectory sample into a compact representation and generate a new trajectory sequence in the learned regular pattern. An anomaly score is computed based on the deviation of the generated trajectory from the test sample. Finally, we can find out the anomalous trajectories with an adaptive threshold. We evaluate the proposed method on two real-world traffic datasets and the experiments show favorable results against state-of-the-art algorithms. This paper\u27s research on sequence-to-sequence regularity learning can provide theoretical and practical support for pattern anomaly detection

Autonomous Abnormal Behaviour Detection Using Trajectory Analysis

Abnormal behaviour detection has attracted signification amount of attention in the past decade due to increased security concerns around the world. The amount of data from surveillance cameras have exceeded human capacity and there is a greater need for anomaly detection systems for crime monitoring. This paper proposes a solution to this problem in a reception area context by using trajectory extraction through Gaussian Mixture Models and Kalman Filter for data association. Here, trajectory analysis was performed on extracted trajectories to detect four different anomalies such as entering staff area, running, loitering and squatting down. The developed anomaly detection algorithms were tested on videos captured at Asia Pacific University’s reception area. These algorithms were able to achieve a promising detection accuracy of 89% and a false positive rate of 4.52%

Anomaly detection via short local trajectories

Trajectory provides an important motion cue in describing the behavior of the moving object and can be used effectively for anomaly detection. But traditional trajectories or tracklets used for analysis have limitations due to various tracking irregularities. In this paper, we propose a novel idea of detecting anomalies in a video, based on short history of a region in motion. These histories are defined as short local trajectories (SLT). In contrast to traditional tracklets, these SLTs are extracted for super-pixels belonging to foreground objects. This captures both spatial and temporal information of a candidate moving object. Furthermore, the proposed trajectory extraction is suitable across videos having different crowd density, occlusions, etc. The trajectories of persons/objects at a particular location under usual condition have certain attributes. Thus, we have used Hidden Markov Model (HMM) for characterizing the usual trajectory patterns for each defined region. The proposed algorithm takes SLTs as observations and measures the likelihood for each super-pixel of being anomaly based on learned HMMs. In order to avoid the influence of noisy trajectories, we have computed spatial consistency measure for each SLT based on the neighboring trajectories. Thus, anomalies detected by the proposed approach are highly localized as demonstrated from the experiments conducted on three anomaly datasets, namely UCSD Pedl, Ped2 and a newly proposed CHUK-Crowd Anomaly Dataset. (C) 2017 Elsevier B.V. All rights reserved