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,
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 residual data, our proposed method significantly reduces the
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 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.

该研究介绍了一种基于压缩域的随机扰动方法，可以从 HEVC 比特流中重建图像，并在交通监控应用中取得了很好的效果，准确率非常高，速度可以提高 56%, 数据大小也更有效率。