Abstract: In video analysis，it usually needs to decode and reconstruct the video sequence before any higher level processing such as classification or detection.However，sometimes a video analysis needs to be proceeded without revealing sensitive information，e.g.the identity of people.This paper proposes a new encoding scheme which enables object-tracking without reconstructing the video sequence.According to compressive sensing theory，encoding a video sequence into a few pseudo-random projections of each frame is reasonable due to the sparsity in the interested information.Then the decoder will exploit the sparsity of background-subtracted image to recover the foreground target.Taking a prior knowledge as target position which is estimated by a particle filter，it is possible to improve the reconstruction of foreground target.As expected，this encoding scheme has capability of privacy-protection as well as security.

Key words: compressive sensing, object tracking, particle filtering, reweighted l₁ minimization

摘要： 视频分析通常在分类或检测等高级任务之前解码并重构视频序列。但是，有时希望只进行视频分析而不暴露敏感信息，例如人员身份。提出了一个能够跟踪目标而不需要重构视频序列的编码方案。根据压缩感知理论，用每帧的少量伪随机投影编码一个视频序列。解码器利用背景消除图像的稀疏性重构前景目标。以粒子滤波器估计的目标位置作为先验知识，可以改进前景目标位置的重构。该编码方案同时具有隐私保护和安全加密功能。

关键词: 压缩感知, 目标跟踪, 粒子滤波, 重新加权l₁最小化