Vehicle License Plate Detection Using Only Block Partitioning Structure of the High Efficiency Video Coding (HEVC)

Abstract

In the field of video analytics, particularly traffic surveillance, there is a growing need for efficient and effective methods for processing and understanding video data. Traditional full video decoding techniques can be computationally intensive and time-consuming, leading researchers to explore alternative approaches in the compressed domain. This study introduces a novel random perturbation-based compressed domain method for reconstructing images from High Efficiency Video Coding (HEVC) bitstreams, specifically designed for traffic surveillance applications. To the best of our knowledge, our method is the first to propose substituting random perturbations for residual values, thereby creating a condensed representation of the original image while retaining information relevant to video understanding tasks, particularly focusing on vehicle detection and classification as key use cases.By not using any residual data, our proposed method significantly reduces the amount of data needed in the image reconstruction process, allowing for more efficient storage and transmission of information. This is particularly important when considering the vast amount of video data involved in surveillance applications. Applied to the public BIT-Vehicle dataset, we demonstrate a significant increase in the reconstruction speed compared to the traditional full decoding approach, with our proposed random perturbation-based method being approximately 56% faster than the pixel domain method. Additionally, we achieve a detection accuracy of 99.9%, on par with the pixel domain method, and a classification accuracy of 96.84%, only 0.98% lower than the pixel domain method. Furthermore, we showcase the significant reduction in data size, leading to more efficient storage and transmission. Our research establishes the potential of compressed domain methods in traffic surveillance applications, where speed and data size are critical factors. The study's findings can be extended to other object detection tasks, such as pedestrian detection, and future work may investigate the integration of compressed and pixel domain information, as well as the extension of these methods to the full video decoding process, encompassing both intra and inter encoded bitstreams.

Compressed domain classification of remote sensing scene images based on sub-band data fusion

“…They take High Efficiency Video Coding (HEVC) based compressed video sequences as input and detect license plate without full decompression. Similar to that work, Beratoglu et al 14 use the block partitioning structure of the HEVC standard to find the license plate location of the given video. Using region information and motion vectors of the given video streams, which are compressed with HEVC format to find anomalies worked by C ¸avaş et al 13 Most of the compressed domain image analysis is based on wavelet transforms.…”

Vehicle License Plate Detector in Compressed Domain

“…Let A be the set of boundary pixel locations of coding units (CUs) corresponding to the image I. An image I is converted into a binary image using (1).…”