Multilevel Depth and Image Fusion for Human Activity Detection

• Published in: IEEE transactions on cybernetics Vol. 43; no. 5; pp. 1383 - 1394
• Main Authors: Bingbing Ni, Yong Pei, Moulin, Pierre, Shuicheng Yan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Actigraphy - instrumentation; Actigraphy - methods; Action recognition and localization; Algorithms; Artificial Intelligence; Computer Peripherals; Computer Simulation; Computer Systems; Context modeling; depth sensor; Feature extraction; Gray-scale; Human motion; Humans; Image detection; Image Enhancement - instrumentation; Image Enhancement - methods; Image processing; Image recognition; Imaging, Three-Dimensional - methods; Joints; Pattern Recognition, Automated - methods; Position (location); Recognition; spatial and temporal context; Studies; Subtraction Technique; Transducers; Video; Video Games; Visual; Visualization; Whole Body Imaging - instrumentation; Whole Body Imaging - methods
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2013.2276433

Recognizing complex human activities usually requires the detection and modeling of individual visual features and the interactions between them. Current methods only rely on the visual features extracted from 2-D images, and therefore often lead to unreliable salient visual feature detection and inaccurate modeling of the interaction context between individual features. In this paper, we show that these problems can be addressed by combining data from a conventional camera and a depth sensor (e.g., Microsoft Kinect). We propose a novel complex activity recognition and localization framework that effectively fuses information from both grayscale and depth image channels at multiple levels of the video processing pipeline. In the individual visual feature detection level, depth-based filters are applied to the detected human/object rectangles to remove false detections. In the next level of interaction modeling, 3-D spatial and temporal contexts among human subjects or objects are extracted by integrating information from both grayscale and depth images. Depth information is also utilized to distinguish different types of indoor scenes. Finally, a latent structural model is developed to integrate the information from multiple levels of video processing for an activity detection. Extensive experiments on two activity recognition benchmarks (one with depth information) and a challenging grayscale + depth human activity database that contains complex interactions between human-human, human-object, and human-surroundings demonstrate the effectiveness of the proposed multilevel grayscale + depth fusion scheme. Higher recognition and localization accuracies are obtained relative to the previous methods.