The increasing illegal parking has become more and more serious. Nowadays the methods of detecting illegally parked vehicles are based on background segmentation. However, this method is weakly robust and sensitive to environment. Benefitting from deep learning, this paper proposes a novel illegal vehicle parking detection system. Illegal vehicles captured by camera are firstly located and classified by the famous Single Shot MultiBox Detector (SSD) algorithm. To improve the performance, we propose to optimize SSD by adjusting the aspect ratio of default box to accommodate with our dataset better. After that, a tracking and analysis of movement is adopted to judge the illegal vehicles in the region of interest (ROI). Experiments show that the system can achieve a 99% accuracy and real-time (25FPS) detection with strong robustness in complex environments.Comment: 5pages,6figure

Fast and accurate vehicle detection in unmanned aerial vehicle (UAV) imagery is a meaningful but challenging task, playing an important role in a wide range of applications. Due to its tiny size, few features, variable scales and imbalance vehicle sample problems in UAV imagery, current deep learning methods used in this task cannot achieve a satisfactory performance both in accuracy and speed, which is obvious a classical trade-off problem. In this paper, we propose a single-shot vehicle detector, which focuses on accurate and real-time vehicle detection in UAV imagery. We make contributions in the following two aspects: 1) presenting a multi-scale feature fusion module to combine the high resolution but semantically weak features with the low resolution but semantically strong features, aiming to introduce context information to enhance the feature representation of the small vehicles; 2) proposing a dynamic training strategy (DTS) which constructs the network to learn more discriminative features of hard examples, via using cross entropy and focal loss function alternately. Experimental results show that our method can achieve 90.8% accuracy in UAV images and can run at 59 FPS on a single NVIDIA 1080Ti GPU for the small vehicle detection in UAV images.

GCN-based methods have achieved remarkable performance in skeleton-based action recognition. However, existing methods have not explicitly attempted to remove temporal and spatial redundancy that might introduce additional computational costs. Inspired by the fact that humans always tend to glimpse at overall motion and then zoom into the most important spatio-temporal regions, we propose a Spatio Temporal Focused Dynamic Network (STFD-Net) trained with reinforcement learning for skeleton-based action recognition. Specifically, we first propose a global extractor with Skeleton Pooling Module (SPM) to enable the network to focus on overall motion information with a refined skeleton structure. Then, a local extractor, containing pair-wise part partition, tubelet proposal network, and Partition-Grouped Module (PGM), is proposed to extract local motion details as a complement to the overall motion information. Finally, the dynamic classifier utilizes a recurrent neural network to dynamically terminate the process once the network is adequately confident. Extensive experiments have demonstrated that the proposed network achieves SOTA level performance with lower computational cost on the NTU 60 and NTU 120 dataset.