Abstract
Camera-based action recognition plays a key role in diverse computer vision applications such as human computer interaction. This paper proposes a new action recognition approach using multi-directional projected depth motion map based motion descriptors. First, for the input depth video sequence, all the depth frames in the video are projected onto multiple planes to form the projected images. The absolute difference between two consecutive projected images is accumulated through the entire depth video for establishing maps from multiple views. Then, the local motion consistency of the map is examined to form a histogram of local binary patterns, which are then concatenated and further incorporated into a kernel-based extreme learning machine for action recognition. In contrast to that only three directions are used to calculated the projected depth images for motion feature extraction in the conventional approaches, the proposed approach is able to provide an effective and flexible framework to examine the depth motion maps in multiple projected directions. The proposed approach is evaluated in the well-known MSRA action and gesture video benchmark datasets to demonstrate its superior performance.
Similar content being viewed by others
References
Cai L, Zhu J, Zeng H, Chen J, Cai C, Ma KK (2018) HOG-assisted deep feature learning for pedestrian gender recognition. J Franklin Inst 355(4):1991–2008
Chaquet JM, Carmona EJ, Fernández-Caballero A (2013) A survey of video datasets for human action and activity recognition. Comput Vis Image Underst 117(6):633–659
Chen C, Liu K, Kehtarnavaz N (2016) Real-time human action recognition based on depth motion maps. J Real-Time Image Process 12(1):155–163
Chen J, Wang B, Zeng H, Cai C, Ma KK (2017) Sum-of-gradient based fast intra coding in 3d-hevc for depth map sequence. J Vis Commun Image Represent 48:329–339
Chen T, Chiu MC (2018) Smart technologies for assisting the life quality of persons in a mobile environment: a review. J Amb Intel Hum Comp 9(2):319–327
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: Theory and applications. Neurocomputing 70(1):489–501
Jia C, Fu Y (2016) Low-rank tensor subspace learning for rgb-d action recognition. IEEE Trans Image Process 25(10):4641–4652
Khaire P, Kumar P, Imran J (2018) Combining CNN streams of RGB-D and skeletal data for human activity recognition. Pat Recogn Lett. https://doi.org/10.1016/j.patrec.2018.04.035
Kläser A, Marszałek M, Schmid C (2008) A spatio-temporal descriptor based on 3D-gradients. In: Everingham M, Needham C, Fraile R (eds) British Machine Vision Conference, Leeds, Royaume-Uni, pp 1–10
Kurakin A, Zhang Z, Liu Z (2012) A real time system for dynamic hand gesture recognition with a depth sensor. In: European Signal Processing Conference, pp 1975–1979
Li W, Zhang Z, Liu Z (2010) Action recognition based on a bag of 3D points. IEEE Conf Comput Vis Pattern Recognition:9–14
Liang B, Zheng L (2015) A survey on human action recognition using depth sensors. In: Int. Conf. on Digital Image Computing: Techniques and Applications, pp 1–8
Liu M, Liu H, Chen C (2017) Robust 3D action recognition through sampling local appearances and global distributions. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2017.2786868
Liu Z, Huang J, Han J, Bu S, Lv J (2017b) Human motion tracking by multiple rgbd cameras. IEEE Trans Circ Syst Video Technol 27(9):2014–2027
Madany N, He Y, Guan L (2016) Human action recognition via multiview discriminative analysis of canonical correlations. In: IEEE Int. Conf. on Image Processing, pp 4170–4174
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Oreifej O, Liu Z (2013) HON4D: Histogram of oriented 4D normals for activity recognition from depth sequences. IEEE Conf Comput Vis Pattern Recognition:716–723
Sathyanarayana S, Satzoda RK, Sathyanarayana S, Thambipillai S (2018) Vision-based patient monitoring: a comprehensive review of algorithms and technologies. J Amb Intell Hum Comp 9(2):225–251
Tian J, Chen L (2017) Abnormal motion detection in video using statistics of spatiotemporal local kinematics pattern. In: IEEE Int. Conf. on Image Processing, Beijing, China, pp 2065–2068
Veeriah V, Zhuang N, Qi G (2015) Differential recurrent neural networks for action recognition. In: IEEE Int. Conf. on Computer Vision, pp 4041–4049
Wang J, Liu Z, Chorowski J, Chen Z, Wu Y (2012) Robust 3D action recognition with random occupancy patterns. In: European Conf. on Computer Vision, pp 872–885
Wang J, Liu Z, Wu Y, Yuan J (2012) Mining actionlet ensemble for action recognition with depth cameras. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp 1290–1297
Wang P, Li W, Gao Z, Zhang J, Tang C, Ogunbona PO (2016) Action recognition from depth maps using deep convolutional neural networks. IEEE Trans Hum Mach Syst 46(4):498–509
Xia L, Aggarwal JK (2013) Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp 2834–2841
Yang X, Zhang C, Tian Y (2012) Recognizing actions using depth motion maps-based histograms of oriented gradients. In: ACM Int. Conf. on Multimedia, pp 1057–1060
Yu M, Liu L, Shao L (2016) Structure-preserving binary representations for RGB-D action recognition. IEEE Trans Pattern Anal Mach Intell 38(8):1651–1664
Zhang S, Wei Z, Nie J, Huang L, Wang S, Li Z (2017) A review on human activity recognition using vision-based method. J Healthcare Eng https://doi.org/10.1155/2017/3090343
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Satyamurthi, S., Tian, J. & Chua, M.C.H. Action recognition using multi-directional projected depth motion maps. J Ambient Intell Human Comput 14, 14767–14773 (2023). https://doi.org/10.1007/s12652-018-1136-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-018-1136-1