DETECTION OF PERSONS AND HEIGHT ESTIMATION IN VIDEO SEQUENCE

The principal goal of this paper is the design and subsequent development of a solution for visual monitoring of specific area. Monitoring includes detection of movement and detection of person in the video sequence. Further additional information is to be extracted, i.e. the number of persons in the area and the height of subjects. Authors of paper propose own solution based on prior comparative analysis of current works and design mobile solution, where the development board handles all the data processing. Intel Galileo development board was selected. Implementation and subsequent testing proves the hardware and software solution to be fully functional

Automatic Intruder Combat System: A way to Smart Border Surveillance

Security and safeguard of international borders have always been a dominant issue for every nation. A large part of a nation’s budget is provided to its defense system. Besides wars, illegal intrusion in terms of terrorism is a critical matter that causes severe harm to nation’s property. In India’s perspective, border patrolling by Border Security Forces (BSF) has already been practiced from a long time for surveillance. The patrolling parties are equipped with high-end surveillance equipments but yet an alternative to the ply of huge manpower and that too in harsh environmental conditions hasn’t been in existence. An automatic mechanism for smart surveillance and combat is proposed in this paper as a solution to the above-discussed problems. Smart surveillance requires automatic intrusion detection in the surveillance video, which is achieved by using optical flow information as motion features for intruder/human in the scene. The use of optical flow in the proposed smart surveillance makes it robust and more accurate. Use of a simple horizontal feature for fence detection makes system simple and faster to work in real-time. System is also designed to respond against the activities of intruders, of which auto combat is one kind of response

Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models

Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine) for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running). Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM). H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU), H-ELM’s training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU)