Abstract
Of late, human activity recognition (HAR) in video has generated much interest. A fundamental step is to develop a computational representation of interactions. Human body is often abstracted using minimum bounding rectangles (MBRs) and approximated as a set of MBRs corresponding to different body parts. Such approximations assume each MBR as an independent entity. This defeats the idea that these are parts of the whole body. A representation schema for interaction between entities, each of which is considered as set of related rectangles or what is referred to as extended objects holds promise. We propose an efficient representation schema for extended objects together with a simple recursive algorithm to extract spatial information. We evaluate our approach and demonstrate that, for HAR, the spatial information thus extracted leads to better models compared to CORE9 [1] a compact and comprehensive representation schema for video understanding.
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We use I-frames obtained using the tool ffmpeg as keyframes, http://www.ffmpeg.org.
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Kalita, S., Karmakar, A., Hazarika, S.M. (2017). Comprehensive Representation and Efficient Extraction of Spatial Information for Human Activity Recognition from Video Data. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-2107-7_8
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DOI: https://doi.org/10.1007/978-981-10-2107-7_8
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