Computer vision based vehicle tracking as a complementary and scalable approach to RFID tagging

TL;DR

This paper presents a computer vision-based vehicle tracking system that complements RFID tagging, using object detection and OCR within a finite-state machine framework to improve scalability and interpretability in vehicle monitoring.

Contribution

The study introduces an interpretable vehicle tracking algorithm leveraging computer vision models and finite-state machines, providing an alternative to RFID tagging for scalable vehicle monitoring.

Findings

Detection rate is affected by vehicle speed and type.

Highest detection accuracy occurs with restricted vehicle movement.

Major errors stem from illegible text, blur, occlusion, and out-of-vocab characters.

Abstract

Logging of incoming/outgoing vehicles serves as a piece of critical information for root-cause analysis to combat security breach incidents in various sensitive organizations. RFID tagging hampers the scalability of vehicle tracking solutions on both logistics as well as technical fronts. For instance, requiring each incoming vehicle(departmental or private) to be RFID tagged is a severe constraint and coupling video analytics with RFID to detect abnormal vehicle movement is non-trivial. We leverage publicly available implementations of computer vision algorithms to develop an interpretable vehicle tracking algorithm using finite-state machine formalism. The state-machine consumes input from the cascaded object detection and optical character recognition(OCR) models for state transitions. We evaluated the proposed method on 75 video clips of 285 vehicles from our system deployment site. We observed that the detection rate is most affected by the speed and the type of vehicle. The highest detection rate is achieved when the vehicle movement is restricted to follow a movement restrictions(SOP) at the checkpoint similar to RFID tagging. We further analyzed 700 vehicle tracking predictions on live-data and identified that the majority of vehicle number prediction errors are due to illegible-text, image-blur, text occlusion and out-of-vocab letters in vehicle numbers. Towards system deployment and performance enhancement, we expect our ongoing system monitoring to provide evidences to establish a higher vehicle-throughput SOP at the security checkpoint as well as to drive the fine-tuning of the deployed computer-vision models and the state-machine to establish the proposed approach as a promising alternative to RFID-tagging.