
Proceedings Paper
Team activity analysis and recognition based on Kinect depth map and optical imagery techniquesFormat | Member Price | Non-Member Price |
---|---|---|
$17.00 | $21.00 |
Paper Abstract
Kinect cameras produce low-cost depth map video streams applicable for conventional surveillance systems. However,
commonly applied image processing techniques are not directly applicable for depth map video processing. Kinect depth
map images contain range measurement of objects at expense of having spatial features of objects suppressed. For
example, typical objects' attributes such as textures, color tones, intensity, and other characteristic attributes cannot be
fully realized by processing depth map imagery. In this paper, we demonstrate application of Kinect depth map and
optical imagery for characterization of indoor and outdoor group activities. A Casual-Events State Inference (CESI)
technique is proposed for spatiotemporal recognition and reasoning of group activities. CESI uses an ontological scheme
for representation of casual distinctiveness of a priori known group activities. By tracking and serializing distinctive
atomic group activities, CESI allows discovery of more complex group activities. A Modified Sequential Hidden
Markov Model (MS-HMM) is implemented for trail analysis of atomic events representing correlated group activities.
CESI reasons about five levels of group activities including: Merging, Planning, Cooperation, Coordination, and
Dispersion. In this paper, we present results of capability of CESI approach for characterization of group activities taking
place both in indoor and outdoor. Based on spatiotemporal pattern matching of atomic activities representing a known
group activities, the CESI is able to discriminate suspicious group activity from normal activities. This paper also
presents technical details of imagery techniques implemented for detection, tracking, and characterization of atomic
events based on Kinect depth map and optical imagery data sets. Various experimental scenarios in indoors and outdoors
(e.g. loading and unloading of objects, human-vehicle interactions etc.,) are carried to demonstrate effectiveness and
efficiency of the proposed model for characterization of distinctive group activities.
Paper Details
Date Published: 17 May 2012
PDF: 12 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83920W (17 May 2012); doi: 10.1117/12.919946
Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)
PDF: 12 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83920W (17 May 2012); doi: 10.1117/12.919946
Show Author Affiliations
Vinayak Elangovan, Tennessee State Univ. (United States)
Vinod K. Bandaru, Tennessee State Univ. (United States)
Vinod K. Bandaru, Tennessee State Univ. (United States)
Amir Shirkhodaie, Tennessee State Univ. (United States)
Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)
© SPIE. Terms of Use
