This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian. The challenge focuses on the problem of precise localization of human faces and bodies, and accurate association of identities. It comprises of three tracks: (i) WIDER Face which aims at soliciting new approaches to advance the state-of-the-art in face detection, (ii) WIDER Pedestrian which aims to find effective and efficient approaches to address the problem of pedestrian detection in unconstrained environments, and (iii) WIDER Person Search which presents an exciting challenge of searching persons across 192 movies. In total, 73 teams made valid submissions to the challenge tracks. We summarize the winning solutions for all three tracks. and present discussions on open problems and potential research directions in these topics.
In view of the inherent non-linearity, complexity, susceptibility to external wind, wave, and current interference of under-driven ships, and the difficulty of adjusting and adjusting control parameters, to improve the performance of ship’s autopilot, a kind of RBF neural network sliding mode variable structure PID controller is designed. Traditional PID control is sensitive to parameter changes, online tuning is difficult, and easy to overshoot. In order to solve this problem, combining the variable structure characteristics of PID, a differential compensation term is added to the integral term to convert the PID control parameters into three parameters with more obvious physical meanings, and then combined with the RBF neural network learning and identification function to realize online tuning and adaptive control of ship control parameters. Using MATLAB software to simulate the container ship “MV KOTA SEGAR” MMG model shows that the designed RBF neural network sliding mode PID controller can effectively eliminate the ship’s lateral deviation caused by external interference such as wind, waves, currents, etc., with high control accuracy,robustness and strong adaptability.
This article introduces a cascaded multitask framework to improve the performance of person search by fully utilizing the combination of pedestrian detection and person re-identification tasks. Inspired by Faster R-CNN, a Pre-extracting Net is used in the front part of the framework to produce the low-level feature maps of a query or gallery. Then, a well-designed Pedestrian Proposal Network called Deformable Pedestrian Space Transformer is introduced with affine transformation combined by parameterized sampler as well as deformable pooling dealing with the challenge of spatial variance of person re-identification. At last, a Feature Sharing Net, which consists of a convolution net and a fully connected layer, is applied to produce output for both detection and re-identification. Moreover, we compare several loss functions including a specially designed Online Instance Matching loss and triplet loss, which supervise the training process. Experiments on three data sets including CUHK-SYSU, PRW and SJTU318 are implemented and the results show that our work outperforms existing frameworks.
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