Abstract
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.
Highlights
With the rapid development of deep learning-based methods, object detection accuracy has increased in almost all areas of image processing, including license plate recognition (LPR) [2], [3]
HIGH EFFICIENCY VIDEO CODING (HEVC) High efficiency video coding (HEVC), known as H.265, is a video compression format which is designed as a successor to the previous H.264 video compression format
The future work is combining this study with LP recognition
Summary
With the rapid development of deep learning-based methods, object detection accuracy has increased in almost all areas of image processing, including license plate recognition (LPR) [2], [3]. Deep learning networks give high performance whereas they require high processing power. The second way, is to transfer data to a center and process it there with powerful computers. The first way increases the cost of the image processing unit, whereas, the second way increases the cost of data transmission and includes risks such as disconnection. For both ways, all efforts to reduce the amount of data to be processed or transmitted are especially important. Numerous IP camera manufacturers include HEVC support by default
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