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- 2018
基于局部压缩感知的行为识别
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Abstract:
压缩感知在目标跟踪领域已取得成功应用，但其在行为识别领域的研究尚不成熟.该文提出了局部压缩感知的思想，结合压缩跟踪与质心定位，实现了视频目标行为的有效识别。局部压缩感知是选定行为敏感区域进行压缩跟踪，基于区域质心轨迹和速度的计算与分类，对目标行为进行认知计算。实验结果表明：借助局部压缩感知，能实现一些特殊的全局目标行为（如奔跑、跌倒等）和局部目标行为（如微笑、眨眼等）的识别，并保证了识别率及识别精度。因此，该文提出的局部压缩感知方法在视频监控目标行为识别领域的应用具有一定的探索意义与研究价值。
Abstract：Compressive sensing has been successfully applied in the field of target tracking but not for behavior recognition. This paper presents a locally compressive sensing algorithm for behavior analysis which combines compressive tracking and centroid localization for recognition of video object behavior. Local compressive sensing selects a behavior-sensitive area for compressive tracking which characterizes target behavior based on classification of the object trajectory and local centroid velocity. Tests show that locally compressive sensing can accurately recognize global behavior such as running and falling and local behavior such as smiles and blinking. Therefore, the locally compressive sensing method is of great value that can be used for video surveillance and behavior recognition.
| [1] | DEVANNE M, BERRETTI S, PALA P, et al. Motion segment decomposition of RGB-D sequences for human behavior understanding[J]. Pattern Recognition, 2017, 61:222-233. |
| [2] | WANG Y, CHEN H, LI S, et al. Object tracking by color distribution fields with adaptive hierarchical structure[J]. Visual Computer, 2017, 33(2):1-13. |
| [3] | CHEN Y, SHEN C. Performance analysis of smartphone-sensor behavior for human activity recognition[J]. IEEE Access, 2017, 5(3):3095-3110. |
| [4] | DING X, CHEN W, WASSELL I J. Compressive sensing reconstruction for video:An adaptive approach based on motion estimation[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2017, 27(7):1406-1420. |
| [5] | LAUE H E A. Demystifying compressive sensing[J]. IEEE Signal Processing Magazine, 2017, 34(4):171-176. |
| [6] | LIU T, QIU T, DAI R, et al. Nonlinear regression A<sup>*</sup>OMP for compressive sensing signal reconstruction[J]. Digital Signal Processing, 2017, 69:11-21. |
| [7] | KITAMURA T, IZUMI K, NAKAJIMA K, et al. Microlensed image centroid motions by an exotic lens object with negative convergence or negative mass[J]. Physical Review D, 2014, 89(8):1-2. |
| [8] | CAMPANA R, MASSARO E, BERNIERI E, et al. Application of the MST clustering to the high energy, γ-ray sky. I-New possible detection of high-energy, γ-ray emission associated with BL Lac objects[J]. Astrophysics and Space Science, 2015, 360(2):1-10. |
| [9] | MINGHU W U, ZHU X. Distributed video compressive sensing reconstruction by adaptive PCA sparse basis and nonlocal similarity[J]. Ksii Transactions on Internet & Information Systems, 2014, 8(8):2851-2865. |
| [10] | GU Y, GOODMAN N A. Information-theoretic compressive sensing kernel optimization and Bayesian Cramér-Rao bound for time delay estimation[J]. IEEE Transactions on Signal Processing, 2017, 65(17):4525-4537. |
| [11] | HEGDE C, INDYK P, SCHMIDT L. Approximation algorithms for model-based compressive sensing[J]. IEEE Transactions on Information Theory, 2015, 61(9):5129-5147. |
| [12] | KUMAR M, BHATNAGAR C. Crowd behavior recognition using hybrid tracking model and genetic algorithm enabled neural network[J]. International Journal of Computational Intelligence Systems, 2017, 10(1):234-246. |
| [13] | BATCHULUUN G, KIM J H, HONG H G, et al. Fuzzy system based human behavior recognition by combining behavior prediction and recognition[J]. Expert Systems with Applications, 2017, 81(9):108-133. |
| [14] | VAN V K, WASHINGTON G. Development of a wearable controller for gesture-recognition-based applications using polyvinylidene fluoride[J]. IEEE Transactions on Biomedical Circuits & Systems, 2017, 11(4):900-909. |
| [15] | ARABLOUEI R. Fast reconstruction algorithm for perturbed compressive sensing based on total least-squares and proximal splitting[J]. Signal Processing, 2017, 130(1):57-63. |
| [16] | JIANG H, DENG W, SHEN Z. Surveillance video processing using compressive sensing[J]. Inverse Problems & Imaging, 2017, 6(2):201-214. |
