Unsupervised Anomalous Trajectory Detection for Crowded Scenes

We present an improved clustering based, unsupervised anomalous trajectory detection algorithm for crowded scenes. The proposed work is based on four major steps, namely, extraction of trajectories from crowded scene video, extraction of several features from these trajectories, independent mean-shift clustering and anomaly detection. First, the trajectories of all moving objects in a crowd are extracted using a multi feature video object tracker. These trajectories are then transformed into a set of feature spaces. Mean shift clustering is applied on these feature matrices to obtain distinct clusters, while a Shannon Entropy based anomaly detector identifies corresponding anomalies. In the final step, a voting mechanism identifies the trajectories that exhibit anomalous characteristics. The algorithm is tested on crowd scene videos from datasets. The videos represent various possible crowd scenes with different motion patterns and the method performs well to detect the expected anomalous trajectories from the scene.

I. Introduction

Computer Vision research aims to converge at human-like abilities to interpret and extract useful information regarding behavioural patterns and anomalies from a descriptive set of visual data. However, human abilities have glaring limitations when it comes to analyzing simultaneously changing signals [1]. A crowd presents itself as a considerably large collection of simultaneously changing parameters, characterized by usual dominant patterns and some observable abnormalities. Safety is the primary reason to understand crowd dynamics and isolate anomalous patterns. With crowd-related violent incidents on the rise, it is paramount that we expand our studies to analyze the intricate and complex nature of crowds. Understanding anomalies in a crowded scene enables better public space design and also allows better surveillance systems to be built. Earlier works like those of Kim et al. [2] used a Mixture of Probabilistic Principal Component Analyzers to learn patterns of local optical flow and then validate the consistency by Markov Random Field. Cong et al. [3] used a multi-scale histogram of Optical Flow as the feature descriptor and used it as the basis for a sparse reconstruction. Ali et al. [4] used Lagrangian Particle Dynamics to model coherent crowd flow as fluid flow.