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.
CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement
