The research on inertial sensor based human action detection and recognition (HADR) is a new area in machine learning. We propose a novel time sequence based interval convolutional neutral networks framework for HADR by combining interesting interval proposals generator and interval-based classifier. Experiments demonstrate the good performance of our method.

Hough-based voting approaches have been widely used to solve many detection problems such as object and action detection. These approaches for action detection cast votes for action classes and positions based on the local spatio-temporal features of given videos. The voting process of each local feature is performed independently of the other local features. This independence enables the method to be robust to occlusions because votes based on visible local features are not influenced by occluded local features. However, such independence makes discrimination of similar motions between different classes difficult and causes the method to cast many false votes. We propose a novel Hough-based action detection method to overcome the problem of false votes. The false votes do not occur randomly such that they depend on relevant action classes. We introduce vote distributions, which represent the number of votes for each action class. We assume that the distribution of false votes include important information necessary to improving action detection. These distributions are used to build a model that represents the characteristics of Hough voting that include false votes. The method estimates the likelihood using the model and reduces the influence of false votes. In experiments, we confirmed that the proposed method reduces false positive detection and improves action detection accuracy when using the IXMAS dataset and the UT-Interaction dataset.

This paper discusses the task of human action detection. It requires not only classifying what type the action of interest is, but also finding actions' spatial-temporal locations in a video. The novelty of this paper lies on two significant aspects. One is to introduce a new graph based representation for the search space in a video. The other is to propose a novel sub-volume search method by Minimum Cycle detection. The proposed method has a low computation complexity while maintaining a high action detection accuracy. It is evaluated on two challenging datasets which are captured in cluttered backgrounds. The proposed approach outperforms other state-of-the-art methods in most situations in terms of both Precision-Recall values and running speeds.