Rejecting Motion Outliers for Efficient Crowd Anomaly Detection

Abstract

Crowd anomaly detection is a key research area in vision-based surveillance. Most of the crowd anomaly detection algorithms are either too slow, bulky, or power-hungry to be applicable for battery-powered surveillance cameras. In this paper, we present a new crowd anomaly detection algorithm. The proposed algorithm creates a feature for every superpixel that includes the contribution from the neighboring superpixels only if their direction of motion conforms with the dominant direction of motion in the region. We also propose using univariate Gaussian discriminant analysis with the K-means algorithm for classification. Our method provides superior accuracy over numerous deep learning-based and handcrafted feature-based approaches. We also present a low-power FPGA implementation of the proposed method. The algorithm is developed such that features are extracted over non-overlapping pixels. This allows gating inputs to numerous modules resulting in higher power efficiency. The maximum energy required per pixel is 2.43 nJ in our implementation. 126.65 Mpixels can be processed per second by the proposed implementation. The speed, power, and accuracy performance of our method make it competitive for surveillance applications, especially battery-powered surveillance cameras.