Ho-Jin Choi

Essential body-joint and atomic action detection for human activity recognition using longest common subsequence algorithm

We present an effective algorithm to detect essential body-joints and their corresponding atomic actions from a series of human activity data for efficient human activity recognition/classification. Our human activity data is captured by a RGB-D camera, i.e. Kinect, where human skeletons are detected and provided by the Kinect SDK. Unique in our approach is the novel encoding that can effectively convert skeleton data into a symbolic sequence representation which allows us to detect the essential atomic actions of different human activities through longest common subsequence extraction. Our experimental results show that, through atomic action detection, we can recognize human activity that consists of complicated actions. In addition, since our approach is simple, our human activity recognition algorithm can be performed in real-time.

A randomized algorithm for natural object colorization

Natural objects often contain vivid color distribution with wide variety of colors. Conventional colorization techniques, on the other hand, produce colors that are relatively flat with little color variation. In this paper, we introduce a randomized algorithm which considers not only the value of target color but also the distribution of target color. In essence, our algorithm paints a color distribution to a region which synthesizes color distribution of a natural object. Our approach models the correlation between intensity and color in HSV color space in terms of H – S, H – V and S – V joint histogram. During the colorization process, we randomly swap and reassign color of a pixel to minimize a cost function that measures color consistency to its neighborhood and intensity‐to‐color correlation captured in the joint histogram. We tested our algorithm extensively on many natural objects and our user study confirms that our results are more vivid and natural compared to results from previous techniques.

Clustering space-time interest points for action representation

This paper presents a novel approach to represent human actions in a video. Our approach deals with the limitation of local representation, i.e. space-time interest points, which cannot adequately represent actions in a video due to lack of global information about geometric relationships among interest points. It adds the geometric relationships to interest points by clustering interest points using squared Euclidean distances, followed by using a minimum hexahedron to represent each cluster. Within each video, we build a multi-dimensional histogram based on the characteristics of hexahedrons in the video for recognition. The experimental results show that the proposed representation is powerful to include the global information on top of local interest points and it successfully increases the accuracy of action recognition.

An interoperable multi-sensor system for healthcare

Pervasive healthcare systems, enabled by information and communication technology (ICT), can allow the elderly and chronically ill to stay at home while being constantly monitored. Patient monitoring can be achieved by sensors and sensor systems that are both worn by the patient and installed in his home environment. There is a large variety of sensors available on the market that can all serve to this purpose. In order to have a system that is independent of the sensors that are used, standardization is the key requirement. This work aims to present a framework for healthcare monitoring systems based on heterogeneous sensors. In order to achieve interoperability, standards are considered in the system design.

Depth consistency evaluation for error-pose detection

With the development of depth sensors, i.e. Kinect, it is now possible to predict human body poses from a depthmap without any manual labeling. The predicted poses can be used as meaningful features for many applications such as human action recognition. However, existing pose estimation algorithms are not perfect, which can seriously affect the performance of its following applications. In this paper, we propose a novel method to detect erroneous poses. Human poses are captured by Kinect SDK which predicts body joints and connects them with straight lines to represent a pose. We observe depth gradient of pixels located on a body part is consistent when the body part is predicted correctly. With this observation, our algorithm examines depth gradients of pixels on each body part. During the depth gradient processing, our algorithm also considers occlusions. Once a sudden change is detected in depth values on a body part, we check whether the gradient is still consistent excluding the sudden change region. We tested our algorithm on many human activities and our experimental results show that our algorithm acceptably detects erroneous poses in real time.