###### Abstract
Human detection is in the early stages in a number of use cases including pedestrian detection, e-health systems, abnormal behavior, person re-identification, driving assistance systems, crowd analysis, gender categorization, smart-video surveillance, human-pose estimation, human tracking, intelligent digital content management, and, finally, human-activity recognition [6,14,15,16,17]. The deep CNN is a dense computing framework in and of itself. With a large number of parameters and higher processing loads, followed by high memory access, energy consumption increases rapidly, thereby making it impossible to adopt the method for compact devices with minimal hardware resources. A feasible approach is a compressive, deep CNN technique for real-time applications and compact low-memory devices, which reduces the number of parameters, the cost of calculation, and power usage by compressing deep CNNs [18]. Over the past few years, the construction of tiny and effective network techniques to detect objects has become a point of discussion in the field of computer vision research. Acceleration and compression techniques are related primarily to the compact configuration of network architecture [19], knowledge distillation [20], network sparsity and pruning [21], and network quantization [22]. Various studies on network compression have advanced network models: for instance, SqueezeNet [23], which is a fire module based architecture; MobileNets [24], a depthwise separable filters based architecture; and, finally, the ShuffileNet [25], a residual structure based network in which channel shuffle strategy and group pointwise convolution were incorporated. Motivated by lightweight architectures, a novel compact model was proposed to detect humans for the portable devices that were absent in the current literature. Tiny-YOLO, which is the tiny version of the YOLO model, is used as the base architecture of this proposed model. YOLO is a faster and more accurate technique compared to other object detection models, and it has been enhanced since its first implementation, which includes v1-YOLO, v2-YOLO, and v3-YOLO. However, these architectures are not suitable for portable devices because of their large sizes and inability to maintain real-time performance in constrained environments. As mentioned, Tiny-YOLO is smaller than these models. However, it failed to achieve high accuracy, and speed remained unsatisfactory for low-memory devices. This article proposes a model called ReSTiNet that is based on Tiny-YOLO. This model reduces the size of the model while simultaneously achieving higher accuracy and boosting detection speeds.The ultimate goal of this article is to develop a more capable human detection model for portable devices. Intelligent surveillance systems that use portable devices with less processing power can easily take advantages of this smaller and lighter model. This improves the performance and capabilities of the system without increasing the cost of the hardware or the amount of processing power it needs. Furthermore, lighter and faster models can be used in low-latency real-time human detection applications. The inspiration for ReSTiNet came from SqueezeNet, which use the fire module in order to decrease the total model parameter numbers and therefore compressed the overall size of the model. Determining the number of fire modules and where in the network they should be placed is one of the parts of integrating the fire module in Tiny-YOLO that presents one of the greatest challenges. The investigation of the residual connection between fire modules is still another key issue that needs to be addressed in order to improve detection accuracies and speeds even more. The useful feature of residual connections in Resnet [26] served as an inspiration for the implementation of residual connections within the fire modules of ReSTiNet. This was performed to ensure that the maximum amount of information flowed and to improve feature propagation throughout the architecture.