Enhanced Spatial–Temporal Map-Based Video Analytic Platform and Its Local- Versus Cloud-Based Deployment with Regional 511 Camera Network

Abstract

This paper explores the cloud- versus server-based deployment scenarios of an enhanced computer vision platform for potential deployment on low-resolution 511 traffic video streams. An existing computer vision algorithm based on a spatial–temporal map and designed for high-angle traffic video like that of NGSIM (Next Generation SIMulation) is enhanced for roadside CCTV traffic camera angles. Because of the lower visual angle, determining the directions, splitting vehicles from occlusions, and identifying lane changes become difficult. A motion-flow-based direction determination method, a bisection occlusion detection and splitting algorithm, and a lane-change tracking method are proposed. The model evaluation is conducted by using videos from multiple cameras from the New Jersey Department of Transportation’s 511 traffic video surveillance system. The results show promising performance in both accuracy and computational efficiency for potential large-scale cloud deployment. The cost analysis reveals that at the current pricing model of cloud computing, the cloud-based deployment is more convenient and cost-effective for an on-demand network assessment. In contrast, the dedicated-server-based deployment is more economical for long-term traffic detection deployment.

Deep spatial‐temporal embedding for vehicle trajectory validation and refinement

“…In the paper (Y. Ge et al, 2022), a video analytic system for closed circuit television traffic cameras was established based on the longitudinal scanline method with cloud infrastructure for potential large-scale deployment. A recent study (Coifman & Li, 2022) uses the spatial-temporal scanline method to evaluate image processing-based vehicle trajectories.…”

“…The authors tested the scanline method at several freeway locations near Barcelona, Spain, and showed that it can avoid complexities and inaccuracies when converting video data into space‐time diagram. In the paper (Y. Ge et al., 2022), a video analytic system for closed circuit television traffic cameras was established based on the longitudinal scanline method with cloud infrastructure for potential large‐scale deployment. A recent study (Coifman & Li, 2022) uses the spatial‐temporal scanline method to evaluate image processing‐based vehicle trajectories.…”