Improved VGG Model for Road Traffic Sign Recognition

Gait recognition is a complicated task due to the existence of co-factors like carrying conditions, clothing, viewpoints, and surfaces which change the appearance of gait more or less. Among those co-factors, clothing analysis is the most challenging one in the area. Conventional methods which are proposed for clothing invariant gait recognition show the body parts and the underlying relationships from them are important for gait recognition. Fortunately, attention mechanism shows dramatic performance for highlighting discriminative regions. Meanwhile, latent semantic analysis is known for the ability of capturing latent semantic variables to represent the underlying attributes and capturing the relationships from the raw input. Thus, we propose a new CNN-based method which leverages advantage of the latent semantic analysis and attention mechanism. Based on discriminative features extracted using attention and the latent semantic analysis module respectively, multi-modal fusion method is proposed to fuse those features for its high fault tolerance in the decision level. Experiments on the most challenging clothing variation dataset: OU-ISIR TEADMILL dataset B show that our method outperforms other state-of-art gait approaches.

Section: Introductionmentioning

confidence: 99%

Attention-Aware Network With Latent Semantic Analysis for Clothing Invariant Gait Recognition

Ling¹,

Wu²,

Li³

et al. 2019

“…The major work for Pipeline video processing includes image preprocessing, defect location and defect feature recognition [Wang, Chen, Qiao et al (2018); Wan, Wei, Jiao et al (2018)]. In this paper, we preprocess the pipeline image using image grayscale conversion, grayscale stretching, smoothing filter and canny edge detection, then extract the defect feature using histograms of oriented gradients (HOG) and visual geometry group network (VGGNet) [Zhou, Liang, Li et al (2018)], and finally locate and recognize the defect with support vector machine (SVM) [Zhang, Li, Lu et al (2016); Wiatowski and Bolcskei (2018)]. The rest of this paper is organized as following.…”

Section: Introductionmentioning

confidence: 99%

Pipeline Defect Detection Cloud System Using Role Encryption and Hybrid Information

Li¹,

Shang²,

Zhang³

et al. 2019

Pipeline defect detection systems collect the videos from cameras of pipeline robots, however the systems always analyzed these videos by offline systems or humans to detect the defects of potential security threats. The existing systems tend to reach the limit in terms of data access anywhere, access security and video processing on cloud. There is in need of studying on a pipeline defect detection cloud system for automatic pipeline inspection. In this paper, we deploy the framework of a cloud based pipeline defect detection system, including the user management module, pipeline robot control module, system service module, and defect detection module. In the system, we use a role encryption scheme for video collection, data uploading, and access security, and propose a hybrid information method for defect detection. The experimental results show that our approach is a scalable and efficient defection detection cloud system.

“…The discovery of GC-Pattern from trajectory streams is critical for real time applications. For example: traffic jam discovery in transportation management, event detection in public security and invasion monitor in military surveillance [Zhou, Liang, Li et al (2018)]. Despite of the wide applications, the discovery of GC-Pattern from trajectory streams is not overlap at any two consecutive timestamps.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams

Zhang¹,

Ji²,

Zhao³

et al. 2019

The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment, which leverages new applications and services. Since the trajectory streams is rapidly evolving, continuously created and cannot be stored indefinitely in memory, the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams. This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models. By processing the trajectory data in current window, the mining algorithm can capture the trend and evolution of moving object clusters pattern. Firstly, the density peaks clustering algorithm is exploited to identify clusters of different snapshots. The stable relationship between relatively few moving objects is used to improve the clustering efficiency. Then, by intersecting clusters from different snapshots, the gradual moving object clusters pattern is updated. The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process. Finally, experiment results on two real datasets demonstrate that our algorithm is effective and efficient.