The PASCAL Visual Object Classes Challenge

A Study on the Autonomous Detection of Impact Craters

Provided is a pedestrian detection method. The pedestrian detection method comprises the following steps that a pedestrian positive sample set and a pedestrian negative sample set needed for training a convolutional neural network are prepared; the sample sets are preprocessed and normalized to conform to a unified standard, and a data file is generated; the structure of the convolutional neural network is designed, training is carried out, and a weight connection matrix is obtained during convergence of the network; self-adaptive background modeling is carried out on videos, information of moving objects in each frame is obtained, coarse selection is carried out on detected moving object regions at first, the regions with height to width ratios unsatisfying requirements are excluded, and candidate regions are generated; each candidate region is input into the convolutional neural network, and whether pedestrians exist is judged.

Pedestrian detection method

In the fight against coronavirus, social distance has proven to be a very effective tool. To minimize the risk of the virus spreading through physical contact or proximity, the public is being advised to limit their contact with one another. It has previously been demonstrated that deep learning can solve a variety of issues. In our proposed system, we utilize Python, image analysis, and other learning techniques to monitor social distance in public areas and offices to corroborate the social distancing protocol. By analysing live video feeds from cameras, this tool will track if people remain within a safe distance from each other. With this tool, it is possible to predict people at malls, company offices, and stores to see if they are at an appropriate distance from one another. 

Machine Learning-Based Social Distance Detection: An Approach Using OpenCV and YOLO Framework

The safety measures in a city are of major concern to its people. A smart surveillance system is necessary to make the safety measures robust and reliable. Currently, there are many object detection methods available for image analysis. Image analysis is only possible when we can detect the number of objects in the image, type of objects, as well as their location. Image analysis methods include Fast RCNN, YOLO, region-based convolution neural network (RCNN), and so on. These object detection methods are based on deep learning (DL) techniques. Video analysis requires the detection of moving objects. These methods involve background subtraction and extracting the foreground objects for motion analysis. There are several deep learning (DL)-based methods for video analysis such as the YOLO algorithm for detecting objects in a video frame and identifying their location. This chapter describes significant object detection methods in images and video that can be used in surveillance systems to improve the safety measures in a city. 

Object Detection Methods for Improving Smart City Safety

Object detection has been known as the core of computer vision and attracted much research attention in recent years especially because of its close relationship with video analysis and image understanding. According to the abundant research on object detection, many traditional object detection methods have been proposed. This paper introduces some famous traditional methods, which are based on SIFT, HOG, SURF, and ORB. However, due to the characteristics of large amount of computation and simple training structure, the traditional detection method has low detection speed. With the fast rise of deeper learning, stronger devices are implemented to address the problems that exist in conventional architectures. In the architecture of the network, training and optimization functions etc., these models are special. In this paper, we review the frameworks for object detection based on deep learning. We begin our review with the methods based on Convolutional Neural Networks. Then typical methods of object detection and some helpful modification to improve detection performance are introduced. Moreover, the methods based on YOLO and SSD are introduced. In fact, despite of the same basis of algorithm or features, the performance and features of different methods are various. Thus in this paper we analyze the features and architecture of each method. This also offers some research to equate various approaches and draw some concrete conclusions. In this paper, we also introduce some typical datasets used for testing or training the object detection model. This paper made a systematic classification and summary in the object detection field, which can be meaningful and useful for the scholars who started to learn about it.

Kunyi Li

Papers

A Review of Object Detection Techniques