Action recognition and video summarization are two important multimedia tasks that are useful for applications such as video indexing and retrieval, video surveillance, human-computer interaction and home intelligence. While many approaches exist in the literature for these two tasks, to date they have always been addressed separately. Instead, in this paper we move from the assumption that these two tasks should be tackled as a joint objective: on the one hand, action recognition can drive the selection of meaningful and informative summaries; on the other, recognizing actions from a summary rather than the entire video can in principle reduce noise and prove more accurate. To this aim, we propose a novel approach for joint action recognition-summarization based on the performing latent structural SVM framework, together with an efficient algorithm for inferring the action and the summary based on the property of sub-modularity. Experimental results on a challenging benchmark, MSR Dai-lyActivity3D, show that the approach is capable of achieving remarkable action recognition accuracy while providing appealing video summaries.

Conventional video tracking operates over RGB or grey-level data which contain significant clues for the identification of the targets. While this is often desirable in a video surveillance context, use of video tracking in privacy-sensitive environments such as hospitals and care facilities is often perceived as intrusive. Therefore, in this work we present a tracker that provides effective target tracking based solely on depth data. The proposed tracker is an extension of the popular Struck algorithm which leverages a structural SVM framework for tracking. The main contributions of this work are novel depth features based on local depth patterns and a heuristic for effectively handling occlusions. Experimental results over the challenging Princeton Tracking Benchmark (PTB) dataset report a remarkable accuracy compared to the original Stuck tracker and other state-of-the-art trackers using depth and RGB data.