High-level feature extraction for crowd behaviour analysis: a computer vision approach.

Abstract

The advent of deep learning has brought in disruptive techniques with unprecedented accuracy rates in so many fields and scenarios. Tasks such as the detection of regions of interest and semantic features out of images and video sequences are quite effectively tackled because of the availability of publicly available and adequately annotated datasets. This paper describes a use case scenario with a deep learning models’ stack being used for crowd behaviour analysis. It consists of two main modules preceded by a pre-processing step. The first deep learning module relies on the integration of YOLOv5 and DeepSORT to detect and track down pedestrians from CCTV cameras’ video sequences. The second module ingests each pedestrian’s spatial coordinates, velocity, and trajectories to cluster groups of people using the Coherent Neighbor Invariance technique. The method envisages the acquisition of video sequences from cameras overlooking pedestrian areas, such as public parks or squares, in order to check out any possible unusualness in crowd behaviour. Due to its design, the system first checks whether some anomalies are underway at the microscale level. Secondly, It returns clusters of people at the mesoscale level depending on velocity and trajectories. This work is part of the physical behaviour detection module developed for the S4AllCities H2020 project.