Detecting feature points on the human body in video frames is a key step for tracking human movements. There have been methods developed that leverage models of human pose and classification of pixels of the body imag...Detecting feature points on the human body in video frames is a key step for tracking human movements. There have been methods developed that leverage models of human pose and classification of pixels of the body image. Yet, occlusion and robustness are still open challenges. In this paper, we present an automatic, model-free feature point detection and action tracking method using a time-of-flight camera. Our method automatically detects feature points for movement abstraction. To overcome errors caused by miss-detection and occlusion, a refinement method is devised that uses the trajectory of the feature points to correct the erroneous detections. Experiments were conducted using videos acquired with a Microsoft Kinect camera and a publicly available video set and comparisons were conducted with the state-of-the-art methods. The results demonstrated that our proposed method delivered improved and reliable performance with an average accuracy in the range of 90 %.The trajectorybased refinement also demonstrated satisfactory effectiveness that recovers the detection with a success rate of 93.7 %. Our method processed a frame in an average time of 71.1 ms.展开更多
We consider the psychophysical experiments in which the test subject’s binary reaction is determined by the prescribed exposure duration to a stimulus and a random variable subjective threshold. For example, when a s...We consider the psychophysical experiments in which the test subject’s binary reaction is determined by the prescribed exposure duration to a stimulus and a random variable subjective threshold. For example, when a subject is exposed to a millimeter wave beam for a prescribed duration, the occurrence of flight action is binary (yes or no). In experiments, in addition to the binary outcome, the actuation time of flight action is also recorded if it occurs;the delay from the initiation time to the actuation time of flight action is the human reaction time, which is not measurable. In this study, we model the random subjective threshold as a Weibull distribution and formulate an inference method for estimating the human reaction time, from data of prescribed exposure durations, binary outcomes and actuation times of flight action collected in a sequence of tests. Numerical simulations demonstrate that the inference of human reaction time based on the Weibull distribution converges to the correct value even when the underlying true model deviates from the inference model. This robustness of the inference method makes it applicable to real experimental data where the underlying true model is unknown.展开更多
文摘Detecting feature points on the human body in video frames is a key step for tracking human movements. There have been methods developed that leverage models of human pose and classification of pixels of the body image. Yet, occlusion and robustness are still open challenges. In this paper, we present an automatic, model-free feature point detection and action tracking method using a time-of-flight camera. Our method automatically detects feature points for movement abstraction. To overcome errors caused by miss-detection and occlusion, a refinement method is devised that uses the trajectory of the feature points to correct the erroneous detections. Experiments were conducted using videos acquired with a Microsoft Kinect camera and a publicly available video set and comparisons were conducted with the state-of-the-art methods. The results demonstrated that our proposed method delivered improved and reliable performance with an average accuracy in the range of 90 %.The trajectorybased refinement also demonstrated satisfactory effectiveness that recovers the detection with a success rate of 93.7 %. Our method processed a frame in an average time of 71.1 ms.
文摘We consider the psychophysical experiments in which the test subject’s binary reaction is determined by the prescribed exposure duration to a stimulus and a random variable subjective threshold. For example, when a subject is exposed to a millimeter wave beam for a prescribed duration, the occurrence of flight action is binary (yes or no). In experiments, in addition to the binary outcome, the actuation time of flight action is also recorded if it occurs;the delay from the initiation time to the actuation time of flight action is the human reaction time, which is not measurable. In this study, we model the random subjective threshold as a Weibull distribution and formulate an inference method for estimating the human reaction time, from data of prescribed exposure durations, binary outcomes and actuation times of flight action collected in a sequence of tests. Numerical simulations demonstrate that the inference of human reaction time based on the Weibull distribution converges to the correct value even when the underlying true model deviates from the inference model. This robustness of the inference method makes it applicable to real experimental data where the underlying true model is unknown.
基金supported by special fund for research institutes supplied the National Key Research and Development Project(2018YFC0807804)the General Program of National Natural Science Foundation of China(42074175)the Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China(2020JM-714,2020JQ-741,2021JQ-949).
