Activity recognition from first-person (ego-centric) videos has recently gained attention due to the increasing ubiquity of the wearable cameras. There has been a surge of efforts adapting existing feature descriptors and designing new descriptors for the first-person videos. An effective activity recognition system requires selection and use of complementary features and appropriate kernels for each feature. In this study, we propose a data-driven framework for first-person activity recognition which effectively selects and combines features and their respective kernels during the training. Our experimental results show that use of Multiple Kernel Learning (MKL) and Boosted MKL in first-person activity recognition problem exhibits improved results in comparison to the state-of-the-art. In addition, these techniques enable the expansion of the framework with new features in an efficient and convenient way.
Fight detection is an important topic for surveillance systems. However, there has been little success in creating an algorithm that can detect fight in surveillance videos with high performance. In this work, we propose a new method for the task of fight detection in surveillance videos. The proposed method relies on a novel motion feature, namely Motion Co-Occurrence Feature (MCF). Firstly, motion vectors are extracted by using block matching algorithm. Secondly, direction and magnitude values of motion vectors are quantized separately. Afterwards, direction and magnitude based MCF is calculated by considering both current and past motion vectors. Experimental results obtained using k-Nearest Neighbor classifier showed that the proposed algorithm can discriminate fight scenes with significantly high accuracy.
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