关键词: 压缩感知, 目标跟踪, 粒子滤波, 重新加权l₁最小化

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