Behavior recognition using Pictorial Structures and DTW

Vajda, Tamás

doi:10.1109/aqtr.2010.5520729

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…The special audio interface chip TLV320AIC10 is adopted in this system, which is a 16-bit low power consumption A/D and D/A audio interface chip (AIC) of TI company [4] [5]. AIC10 is more reliable in performance and more convenient in applications, so it is easy to connect with the interface of micro-programmed control unit (MCU).…”

Section: B the Selection Of Audio Codecmentioning

confidence: 99%

Realization of speech processing and recognition system based on digital signal processor

Wei

Jin

Wen-guang

2013

Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)

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16-bit fixed-point high-speed chip TMS320VC5402 and audio interface chip TLV320AIC10 are adopted in this paper, by which collecting, filtering and compressing data are processed. Final data are put into computer through serial port and received by MATLAB to complete playback, coding, recognition and simulation. Floating-point operation is realized on fixed-point digital signal processor, which has higher precision in calculation and wider signal processing dynamic range comparing with fixed-point realization. The experimental results show that rate of continuous word speech recognition is more than 92%. The new system has advantage of small size, low cost and high scalability, which can be applied to many specific occasions, such as the speech controlling toys and access control system.

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Section: B the Selection Of Audio Codecmentioning

confidence: 99%

Realization of speech processing and recognition system based on digital signal processor

Wei

Jin

Wen-guang

2013

Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)

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“…On the one hand, there have been a large number of studies focused on human social behavior analysis [16] [15][7] [5] and head pose estimation [13][17] [12][11] [14] [10]. In these previous works, some have used head pose estimation to analyze human social behavior [4][3] [6].…”

Section: Related Workmentioning

confidence: 99%

Gaze Estimation in Children's Peer-Play Scenarios

Duan

Lan

Cui

et al. 2013

2013 2nd IAPR Asian Conference on Pattern Recognition

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Gaze is a powerful cue for children's social behavior analysis. In this paper, a novel method is proposed to estimate children's gaze orientation in the experimental data of developmental psychology based on head pose estimation. In consideration of the possible errors of head pose estimation results, temporal information and potential targets are both introduced to improve the results of gaze estimation. At last, this method is evaluated by a dataset of children's peer-play scenarios and the results show that this method has a good performance. According to the experimental valuation and analysis, in a certain peer-play scenario, potential targets are powerful spatial cues for children's gaze estimation and temporal information also provides some cues to improve the estimation results.

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“…There have been a great number of studies on adult social behavior analysis [19] [17] in the field of computer vision. While analyzing adult behavior, the scenarios are usually specific [9][6], such as office room, meeting room and traffic scene.…”

Section: Related Workmentioning

confidence: 99%

Video Based Children's Social Behavior Classification in Peer-Play Scenarios

Tian

Duan

Cui

et al. 2013

2013 2nd IAPR Asian Conference on Pattern Recognition

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Labeling children's play behavior is an important process in children's social behavior analysis which is traditionally done by experienced coders. With the growing volume of data, automatic methods for labeling are increasingly required. This paper presents a novel method to label children's social behavior automatically in peer-play scenarios based on visual attention and mutual interaction computation. In this method, the discrete distribution of children's visual attention is computed based on face pose estimation. Then, the mutual interaction among children is calculated by "Attention Processes", which are continuous periods of time during which a certain child pays attention to the same target. After that, "Solitary Feature" and "Group Feature" are extracted by the mutual interaction of children and children's play behaviors will be classified into 3 types ("Solitary Play", "Parallel Play" and "Group Play") by these two kinds of features. Finally, this method is evaluated by a dataset of children's peer-play scenarios and the results show this method has a good performance in our dataset.

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Behavior recognition using Pictorial Structures and DTW

Cited by 6 publications

References 12 publications

Realization of speech processing and recognition system based on digital signal processor

Realization of speech processing and recognition system based on digital signal processor

Gaze Estimation in Children's Peer-Play Scenarios

Video Based Children's Social Behavior Classification in Peer-Play Scenarios

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