Cameras are being used everywhere for the safety and security of citizens in different countries. Using a machine to detect humans in a photo or a video frame is a very complicated and challenging task. Various techniques have been developed for this purpose, which mainly rely on Artificial Intelligence. This article aims to provide a comprehensive review and analysis of the literatures from a descriptive perspective, which is its main differentiator from the existing survey papers in this area. Firstly, the vision-based human detection techniques and classifiers are elucidated in conjunction with the variants of feature extraction techniques. Secondly, various pros and cons of such techniques are discussed. Then, an investigation has been conducted and reported based on the state-of-the-art human detection descriptors (e.g. Log-Average Miss Rate and accuracy). Although techniques such as Viola-Jones and Speeded-Up Robust Features can detect objects in real-time and overcome Scale-Invariant Feature Transform (SIFT) limitations, they are still sensitive to illuminated conditions. Other techniques such as SIFT, Bag of Words, Orthogonal Moments, and Histogram of oriented Gradients provide other interesting benefits which include insensitivity to occlusion and clutters, simplicity, low-order element construction and invariance to illuminated conditions; nevertheless, they are computationally expensive and sensitive to image rotation. A meticulous review along similar lines revealed that the Deformable Part-based Model performs relatively better due to its ability to deal with particular pose variations and multiple views, occlusion handling (partial) and is application-free while its counterparts focus on only a single aspect. This article highlights and provides a brief description of each available data-sets for human detection research. Various use-cases of human detection systems are also elaborated. Finally, various conclusions are derived based on the conducted review followed by recommendations for future directions and possibilities to further improve the speed and accuracy of human detection systems.

Human detection is a special application of object recognition and is considered one of the greatest challenges in computer vision. It is the starting point of a number of applications, including public safety and security surveillance around the world. Human detection technologies have advanced significantly in recent years due to the rapid development of deep learning techniques. Despite recent advances, we still need to adopt the best network-design practices that enable compact sizes, deep designs, and fast training times while maintaining high accuracies. In this article, we propose ReSTiNet, a novel compressed convolutional neural network that addresses the issues of size, detection speed, and accuracy. Following SqueezeNet, ReSTiNet adopts the fire modules by examining the number of fire modules and their placement within the model to reduce the number of parameters and thus the model size. The residual connections within the fire modules in ReSTiNet are interpolated and finely constructed to improve feature propagation and ensure the largest possible information flow in the model, with the goal of further improving the proposed ReSTiNet in terms of detection speed and accuracy. The proposed algorithm downsizes the previously popular Tiny-YOLO model and improves the following features: (1) faster detection speed; (2) compact model size; (3) solving the overfitting problems; and (4) superior performance than other lightweight models such as MobileNet and SqueezeNet in terms of mAP. The proposed model was trained and tested using MS COCO and Pascal VOC datasets. The resulting ReSTiNet model is 10.7 MB in size (almost five times smaller than Tiny-YOLO), but it achieves an mAP of 63.74% on PASCAL VOC and 27.3% on MS COCO datasets using Tesla k80 GPU.

Human detection is an important task in computer vision. It is one of the most important tasks in global security and safety monitoring. In recent days, Deep Learning has improved human detection technology. Despite modern techniques, there are very few optimal techniques to construct networks with a small size, deep architecture, and fast training time while maintaining accuracy. ReSTiNet is a novel small convolutional neural network that overcomes the problems of network size, detection speed, and accuracy. The developed ReSTiNet contains fire modules by evaluating their number and position in the network to minimize the model parameters and network size. To improve the detection speed and accuracy of ReSTiNet, the residual block within the fire modules is carefully designed to increase the feature propagation and maximize the information flow in the network. The developed approach compresses the well-known Tiny-YOLO architecture while improving the following features: (i) small model size, (ii) faster detection speed, (iii) resolution of overfitting, and (iv) better performance than other compact networks such as SqueezeNet and MobileNet in terms of mAP on the Pascal VOC and MS COCO datasets.