Indoor Human Detection Based on Thermal Array Sensor Data and Adaptive Background Estimation

Low Resolution Thermal Array Sensors are widely used in several applications in indoor environments. In particular, one of these cheap, small and unobtrusive sensors provides a low-resolution thermal image of the environment and, unlike cameras; it is capable to detect human heat emission even in dark rooms. The obtained thermal data can be used to monitor older seniors while they are performing daily activities at home, to detect critical situations such as falls. Most of the studies in activity recognition using Thermal Array Sensors require human detection techniques to recognize humans passing in the sensor field of view. This paper aims to improve the accuracy of the algorithms used so far by considering the temperature environment variation. This method leverages an adaptive background estimation and a noise removal technique based on Kalman Filter. In order to properly validate the system, a novel installation of a single sensor has been implemented in a smart environment: the obtained results show an improvement in human detection accuracy with respect to the state of the art, especially in case of disturbed environments

Moving vehicle detection for automatic traffic monitoring

2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Adaptive background reconstruction for street surveillance

In recent years, adaptive background reconstruction works have found interest in many researchers. However, the existing algorithms that have been proposed by other researchers still in the early stage of development and many aspects need to be improved. In this paper, an adaptive background reconstruction is presented. Past pixel observation is used. The proposed algorithm also has eliminated the need of the pre-training of non-moving objects in the background. The proposed algorithm is capable of reconstructing the background with moving objects in video sequence. Experimental results show that the proposed algorithms are able to reconstruct the background correctly and handle illumination and adverse weather that modifies the background

Intelligent surveillance system for street surveillance

CCTV surveillance systems are widely used as a street monitoring tool in public and private areas. This paper presents a novel approach of an intelligent surveillance system that consists of adaptive background modelling, optimal trade-off features tracking and detected moving objects classification. The proposed system is designed to work in real-time. Experimental results show that the proposed background modelling algorithms are able to reconstruct the background correctly and handle illumination and adverse weather that modifies the background. For the tracking algorithm, the effectiveness between colour, edge and texture features for target and candidate blobs were analysed. Finally, it is also demonstrated that the proposed object classification algorithm performs well with different classes of moving objects such as, cars, motorcycles and pedestrians

A study on Moving Objects Recognization in DIP using thresholiding and other Methods

The digital image processing deals with developing a digital system to performs experiments and operations on a digital image with the use of computer algorithms. An image is nothing more than a 2D mathematical function f(x,y) where x and y are two horizontally and vertically co-ordinates. Object recognition is one of the most important applications of image processing. Vehicle location from a satellite picture or aeronautical picture is a standout amongst the most fascinating and testing research themes from recent years. Vehicle location from satellite picture is one of the utilizations of protest recognition. The activity and jam is expanding ordinary in everywhere throughout the world. Satellites pictures are typically utilized for climate anticipating and geological applications. In this way, Satellites pictures might be additionally useful for the recognizing activity utilizing Image preparing. This theory utilized straightforward morphological acknowledgment strategy for vehicle recognition utilizing picture preparing procedure in Matlab which is best technique for identification of autos, trucks and transports. We can without much of a stretch register the aggregate quantities of vehicles in the coveted zone in the satellite picture and vehicles are appeared under the jumping box as a little spots. Here we look at two calculations like pixel thresholding and Otsu thresholding technique. As indicated by our outcome Pixel level thresholding is superior to Otsu technique

Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference

Low-resolution infrared (IR) Sensors combined with machine learning (ML) can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces. However, the need of executing these applications on Internet of Things (IoT) edge nodes makes energy consumption critical. In this work, we propose an energy-efficient adaptive inference solution consisting of the cascade of a simple wake-up trigger and a 8-bit quantized Convolutional Neural Network (CNN), which is only invoked for difficult-to-classify frames. Deploying such adaptive system on a IoT Microcontroller, we show that, when processing the output of a 8×8 low-resolution IR sensor, we are able to reduce the energy consumption by 37-57% with respect to a static CNN-based approach, with an accuracy drop of less than 2% (83% balanced accuracy)