Convolutional Neural Network Based Approach to Detect Pedestrians in Real-Time videos

Abstract

Pedestrians in the vehicle way are in peril of being hit, along these lines making extreme damage walkers and vehicle inhabitants. Hence, constant person on foot identification was done through a set of recorded videos and the system detects the persons/pedestrians in the given input videos. In this survey, a continuous plan was proposed dependent on Aggregated Channel Features (ACF) and CPU. The proposed technique doesn't have to resize the information picture neither the video quality. We also use SVM with HOG and SVM with HAAR to detect the pedestrians. In addition, the Convolutional Neural Networks (CNN) were trained with a set of pedestrian images datasets and later tested on some test-set of pedestrian images. The analyses demonstrated that the proposed technique could be utilized to distinguish people on foot in the video with satisfactory mistake rates and high prediction accuracy. In this manner, it tends to be applied progressively for any real-time streaming of videos and also for prediction of pedestrians in prerecorded videos.

Visual Relationship-Based Identification of Key Construction Scenes on Highway Bridges

“…It has been verified that the model can complete target detection, visual relationship detection, and output construction scene information as text, realizing the automation and intelligence of identification in the key construction scenes on bridges. In the CSIN model, CNN and DCR are used as the base networks for target detection and relationship detection, respectively, which have been proved to have certain generalization abilities in related literature [39,40]. In addition, the underlying features of scene identification rules in this paper, such as color features, geometric features, and posture features, will not change greatly with different scenes, so they are portable.…”