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

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.

Exploration of an End-to-End Automatic Number-plate Recognition neural network for Indian datasets

Indian vehicle number plates have wide variety in terms of size, font, script and shape. Development of Automatic Number Plate Recognition (ANPR) solutions is therefore challenging, necessitating a diverse dataset to serve as a collection of examples. However, a comprehensive dataset of Indian scenario is missing, thereby, hampering the progress towards publicly available and reproducible ANPR solutions. Many countries have invested efforts to develop comprehensive ANPR datasets like Chinese City Parking Dataset (CCPD) for China and Application-oriented License Plate (AOLP) dataset for US. In this work, we release an expanding dataset presently consisting of 1.5k images and a scalable and reproducible procedure of enhancing this dataset towards development of ANPR solution for Indian conditions. We have leveraged this dataset to explore an End-to-End (E2E) ANPR architecture for Indian scenario which was originally proposed for Chinese Vehicle number-plate recognition based on the CCPD dataset. As we customized the architecture for our dataset, we came across insights, which we have discussed in this paper. We report the hindrances in direct reusability of the model provided by the authors of CCPD because of the extreme diversity in Indian number plates and differences in distribution with respect to the CCPD dataset. An improvement of 42.86% was observed in LP detection after aligning the characteristics of Indian dataset with Chinese dataset. In this work, we have also compared the performance of the E2E number-plate detection model with YOLOv5 model, pre-trained on COCO dataset and fine-tuned on Indian vehicle images. Given that the number Indian vehicle images used for fine-tuning the detection module and yolov5 were same, we concluded that it is more sample efficient to develop an ANPR solution for Indian conditions based on COCO dataset rather than CCPD dataset.