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Spatial information in classification of activity videos

Spatial information describes the relative spatial position of an object in a video. Such information may aid several video analysis tasks such as object, scene, event and activity recognition. This paper studies the effect of spatial information on video activity recognition. The paper firstly performs activity recognition on KTH and Weizmann videos using Hidden Markov Model and k-Nearest Neighbour classifiers trained on Histogram Of Oriented Optical Flows feature. Histogram of Oriented Optical Flows feature is based on optical flow vectors and ignores any spatial information present in a video. Further, in this paper, a new feature set, referred to as Regional Motion Vectors is proposed. This feature like Histogram of Oriented Optical Flow is derived from optical flow vectors; however, unlike Histogram of Oriented Optical Flows preserves any spatial information in a video. Activity recognition was again performed using the two classifiers, this time trained on Regional Motion Vectors feature. Results show that when Regional Motion Vectors is used as the feature set on the KTH dataset, there is a significant improvement in the performance of k-Nearest Neighbour. When Regional Motion Vector is used on the Weizmann dataset, performances of the k-Nearest Neighbour improves significantly for some of the cases and for the other cases, the performance is comparable to when oriented optical flows is used as a feature set. Slight improvement is achieved by Hidden Markov Model on both the datasets. As Histogram of Oriented Optical Flows ignores spatial information and Regional Motion Vectors preserves it, the increase in the performance of the classifiers on using Reginal Motion Vectors instead of Histogram of Oriented Optical Flows illustrates the importance of spatial information in video activity recognition.