The paper presents an analysis of human reaching movements in manipulation of flexible objects. To predict the trajectory of human hand, a minimum crackle criterion has been recently introduced in literature. A different approach is explored in this paper. To explain the trajectory formation, we resort to the minimum hand jerk criterion. First, we show that this criterion matches well experimental data available in literature. Next, we argue that, contrary to the minimum crackle criterion, the minimum hand jerk criterion produces bounded hand velocity profiles for multimass flexible objects. Finally, we present initial experimental results confirming the applicability of the minimum hand jerk criterion to the prediction of reaching movements with multimass objects.
Pedestrian fatalities and injuries most likely occur in vehicle-pedestrian crashes. Meanwhile, engineers have tried to reduce the problems by developing a pedestrian detection function in Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles. However, the system is still not perfect. A remaining problem in pedestrian detection is the performance reduction at nighttime, although pedestrian detection should work well regardless of lighting conditions. This study presents an evaluation of pedestrian detection performance in different lighting conditions, then proposes to adopt multispectral image and deep neural network to improve the detection accuracy. In the evaluation, different image sources including RGB, thermal, and multispectral format are compared for the performance of the pedestrian detection. In addition, the optimizations of the architecture of the deep neural network are performed to achieve high accuracy and short processing time in the pedestrian detection task. The result implies that using multispectral images is the best solution for pedestrian detection at different lighting conditions. The proposed deep neural network accomplishes a 6.9% improvement in pedestrian detection accuracy compared to the baseline method. Moreover, the optimization for processing time indicates that it is possible to reduce 22.76% processing time by only sacrificing 2% detection accuracy.
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