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With the widespread use of installed cameras, video-based monitoringapproaches have seized considerable attention for different purposes likeassisted living. Temporal redundancy and the sheer size of raw videos are thetwo most common problematic issues related to video processing algorithms. Mostof the existing methods mainly focused on increasing accuracy by exploringconsecutive frames, which is laborious and cannot be considered for real-timeapplications. Since videos are mostly stored and transmitted in compressedformat, these kinds of videos are available on many devices. Compressed videoscontain a multitude of beneficial information, such as motion vectors andquantized coefficients. Proper use of this available information can greatlyimprove the video understanding methods' performance. This paper presents anapproach for using residual data, available in compressed videos directly,which can be obtained by a light partially decoding procedure. In addition, amethod for accumulating similar residuals is proposed, which dramaticallyreduces the number of processed frames for action recognition. Applying neuralnetworks exclusively for accumulated residuals in the compressed domainaccelerates performance, while the classification results are highlycompetitive with raw video approaches.

Speeding Up Action Recognition Using Dynamic Accumulation of Residuals in Compressed Domain