Abstract
Human action recognition is an increasingly matured field of study in the recent years, owing to its widespread use in various applications. A number of related research problems, such as feature representations, human pose and body parts detection, and scene/object context, are being actively studied. However, the general problem of video quality—a realistic issue in the face of low-cost surveillance infrastructure and mobile devices, has not been systematically investigated from various aspects. In this paper, we address the problem of action recognition in low-quality videos from a myriad of perspectives: spatial and temporal downsampling, video compression, and the presence of motion blurring and compression artifacts. To increase the resilience of feature representation in these type of videos, we propose to use textural features to complement classical shape and motion features. Extensive results were carried out on low-quality versions of three publicly available datasets: KTH, UCF-YouTube, HMDB. Experimental results and analysis suggest that leveraging textural features can significantly improve action recognition performance under low video quality conditions.
Highlights
Human action recognition is one of the most important research areas in computer vision due to its usefulness in real-world applications such as video surveillance, human computer interaction, and video archival systems
The main idea revolves around the utilization of textural information with conventional shape and motion features to improve the recognition of human actions in low-quality videos
0 bad medium good 0 bad medium good showed that Fisher Vector (FV) encoding is superior to BoVW encoding, but we found that this is untrue most of the time for the evaluated low-quality videos, with the exception of videos from HMDB
Summary
Human action recognition is one of the most important research areas in computer vision due to its usefulness in real-world applications such as video surveillance, human computer interaction, and video archival systems. While there is significant amount of progress in solving these problems, the issue of video quality [1, 2] has received much less research attention. The recognition of human actions from low-quality video is highly challenging as valuable visual information is compromised by various internal and external factors such as low resolution, sampling rate, compression artifacts and motion. The quality of detected interest points is highly dependent on the quality of the video as important points may be missed in cases where video quality is poor. Shape and motion descriptors such as HOG [9], HOF [5, 6], and MBH [7, 8] becomes less discriminative when the quality of video deteriorates; noisy image pixels can cause gradient and Rahman et al EURASIP Journal on Image and Video Processing (2017) 2017:74
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