Human action recognition using simple geometric features and a finite state machine

Dudzńiski, Damian; Kryjak, Tomasz; Mikrut, Zbigniew

doi:10.2478/v10248-012-0079-y

Cited by 2 publications

(1 citation statement)

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“…Rahman et al [ 50 ] proposed to use the surrounding regions of the human silhouette termed as negative space for recognition of human actions from video sequences. In our recent work [ 51 ] the simple but dynamic geometric features like height, width, head and feet positions of the silhouette, combined with a finite state machine, were used for recognition of several human actions.…”

Section: Related Workmentioning

confidence: 99%

Seamless Tracing of Human Behavior Using Complementary Wearable and House-Embedded Sensors

Augustyniak

Smoleń

Mikrut

et al. 2014

Sensors

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This paper presents a multimodal system for seamless surveillance of elderly people in their living environment. The system uses simultaneously a wearable sensor network for each individual and premise-embedded sensors specific for each environment. The paper demonstrates the benefits of using complementary information from two types of mobility sensors: visual flow-based image analysis and an accelerometer-based wearable network. The paper provides results for indoor recognition of several elementary poses and outdoor recognition of complex movements. Instead of complete system description, particular attention was drawn to a polar histogram-based method of visual pose recognition, complementary use and synchronization of the data from wearable and premise-embedded networks and an automatic danger detection algorithm driven by two premise- and subject-related databases. The novelty of our approach also consists in feeding the databases with real-life recordings from the subject, and in using the dynamic time-warping algorithm for measurements of distance between actions represented as elementary poses in behavioral records. The main results of testing our method include: 95.5% accuracy of elementary pose recognition by the video system, 96.7% accuracy of elementary pose recognition by the accelerometer-based system, 98.9% accuracy of elementary pose recognition by the combined accelerometer and video-based system, and 80% accuracy of complex outdoor activity recognition by the accelerometer-based wearable system.

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Section: Related Workmentioning

confidence: 99%