Data compression techniques allow data size to be reduced prior to data transmission and involve decompression upon transfer. This study shows for the first time that license plate (LP) detection can be accomplished without full decompression of the encoded data. Therefore, by determining in advance which images are required for LP recognition, computational costs of the system can be reduced. The proposed approach is realized on High Efficiency Video Coding (HEVC) based compressed video sequences. Two methods are provided that generate images from HEVC attributes. Fully decoded pixel domain images are also generated for comparative purposes from the same encoded data. The YOLO V3 Tiny Object Detector is used in order to detect LPs in the generated images. EnglishLP, a public dataset, is used to interpret the findings in terms of speed and precision and for comparison with previous studies. An additional contribution of the paper is that a new compressed domain LP database has been created and made publicly available, comprising images captured by a commercial license plate recognition system. Using at least two-orders-of-magnitude less amount of data, the proposed compressed domain LP detector achieved similar precision and recall values to those of the state-of-the-art LP detection schemes tested on both datasets. Moreover, the proposed method results in more than 30% saving in inference time. The results suggest that the proposed method can be utilized for rapid video archive searching applications.
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.
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