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Computer vision has been revolutionised in recent years by increased research in convolutional neural networks (CNNs); however, many challenges remain to be addressed in order to ensure fast and accurate image processing when applying these techniques to robotics. These challenges consist of handling extreme changes in scale, illumination , noise,...
Citations
... The faster RCNN (Ren et al., 2016) improves upon the fast RCNN. In the faster RCNN method, the ''Selective Search'' method was replaced by the Region Proposal Network (known as RPN), which is a network to present regions (Brandenburg et al., 2019) and is faster than the RCNN and fast RCNN. ...
Smart surveillance systems are used to monitor specific areas, such as homes, buildings, and borders, and these systems can effectively detect any threats. In this work, we investigate the design of low-cost multiunit surveillance systems that can control numerous surveillance cameras to track multiple objects (i.e., people, cars, and guns) and promptly detect human activity in real time using low computational systems, such as compact or single board computers. Deep learning techniques are employed to detect certain objects to surveil homes/buildings and recognize suspicious and vital events to ensure that the system can alarm officers of relevant events, such as stranger intrusions, the presence of guns, suspicious movements, and identified fugitives. The proposed model is tested on two computational systems, specifically, a single board computer (Raspberry Pi) with the Raspbian OS and a compact computer (Intel NUC) with the Windows OS. In both systems, we employ components, such as a camera to stream real-time video and an ultrasonic sensor to alarm personnel of threats when movement is detected in restricted areas or near walls. The system program is coded in Python, and a convolutional neural network (CNN) is used to perform recognition. The program is optimized by using a foreground object detection algorithm to improve recognition in terms of both accuracy and speed. The saliency algorithm is used to slice certain required objects from scenes, such as humans, cars, and airplanes. In this regard, two saliency algorithms, based on local and global patch saliency detection are considered. We develop a system that combines two saliency approaches and recognizes the features extracted using these saliency techniques with a conventional neural network. The field results demonstrate a significant improvement in detection, ranging between 34% and 99.9% for different situations. The low percentage is related to the presence of unclear objects or activities that are different from those involving humans. However, even in the case of low accuracy, recognition and threat identification are performed with an accuracy of 100% in approximately 0.7 s, even when using computer systems with relatively weak hardware specifications, such as a single board computer (Raspberry Pi). These results prove that the proposed system can be practically used to design a low-cost and intelligent security and tracking system.
... It is trained on objects with different pose, lightening and surrounding clutter from NORB and jittered dataset. But, LeNet fail to classify images in (Brandenburg, et al., 2019). LeNet benefits from the parameter sharing concept in reducing the number of parameters; whereas LeNet contains 340,908 connections, the trained parameters are 60,000. ...
Object classification problem is essential in many applications nowadays. Human can easily classify
objects in unconstrained environments easily. Classical classification techniques were far away from
human performance. Thus, researchers try to mimic the human visual system till they reached the deep
neural networks. This chapter gives a review and analysis in the field of the deep convolutional neural
network usage in object classification under constrained and unconstrained environment. The chapter
gives a brief review on the classical techniques of object classification and the development of bioinspired
computational models from neuroscience till the creation of deep neural networks. A review
is given on the constrained environment issues: the hardware computing resources and memory, the
object appearance and background, and the training and processing time. Datasets that are used to test
the performance are analyzed according to the images environmental conditions, besides the dataset
biasing is discussed.
