Comparative evaluation of stationary foreground object detection algorithms based on background subtraction techniques

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Á. Bayona, J. C. SanMiguel, and J. M. Martínez, "Comparative evaluation of stationary foreground object detection algorithms based on background subtraction techniques" in Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance. AVSS 2009, p. 25 - 30In several video surveillance applications, such as the detection of abandoned/stolen objects or parked vehicles,the detection of stationary foreground objects is a critical task. In the literature, many algorithms have been proposed that deal with the detection of stationary foreground objects, the majority of them based on background subtraction techniques. In this paper we discuss various stationary object detection approaches comparing them in typical surveillance scenarios (extracted from standard datasets). Firstly, the existing approaches based on background-subtraction are organized into categories. Then, a representative technique of each category is selected and described. Finally, a comparative evaluation using objective and subjective criteria is performed on video surveillance sequences selected from the PETS 2006 and i-LIDS for AVSS 2007 datasets, analyzing the advantages and drawbacks of each selected approach.This work has partially supported by the Cátedra UAMInfoglobal ("Nuevas tecnologías de vídeo aplicadas a sistemas de video-seguridad"), the Spanish Administration agency CDTI (CENIT-VISION 2007-1007), by the Spanish Government (TEC2007-65400 SemanticVideo), by the Comunidad de Madrid (S-050/TIC-0223- ProMultiDis), by the Consejería de Educación of the Comunidad de Madrid, and by The European Social Fund

Object Detection and Sorting using IoT

Color based object sorting has a significant impact in food and processing Industries. Hand picking process in sorting the huge number of objects in industry is very common and laborious task, and time consuming as well, which needs many labors and this conventional method is prone to error. The proposed work aims to replace the hand-picking process by Industrial Internet of Things. The goal of the technique is to sort and count the objects in to different bins accord to their color. A Color sensor, TCS 230 will identify the object and with the help of motors they are made to drop into different bins. The identification of the object is made with the help of frequency concept. As it known that different colors have different wave lengths, so are the different frequencies (f=c/?). For each frequency, the motor rotates to different angles and thus container is attached to motor is also made to rotate to a certain angle, and the object is made to drop into the bin by a jerk. This action details regarding number of objects manufactured are sent to the IoT server, where the vendor and customer will know the details remotely. This proposed work finds a wide range of usage in fruit industry (to pick the unripen fruit), in candy industry, in grain industry (to remove the black stones from the grains), in recycling industry.&nbsp

Stationary foreground detection using background subtraction and temporal difference in video surveillance

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Á. Bayona, J. C. SanMiguel, and Martínez, "Stationary foreground detection using background subtraction and temporal difference in video surveillance", in 17th IEEE International Conference on Image Processing, ICIP 2010, p. 4657 - 4660In this paper we describe a new algorithm focused on obtaining stationary foreground regions, which is useful for applications like the detection of abandoned/stolen objects and parked vehicles. Firstly, a sub-sampling scheme based on background subtraction techniques is implemented to obtain stationary foreground regions. Secondly, some modifications are introduced on this base algorithm with the purpose of reducing the amount of stationary foreground detected. Finally, we evaluate the proposed algorithm and compare results with the base algorithm using video surveillance sequences from PETS 2006, PETS 2007 and I-LIDS for AVSS 2007 datasets. Experimental results show that the proposed algorithm increases the detection of stationary foreground regions as compared to the base algorithm.Work supported by the Spanish Government (TEC2007- 65400 SemanticVideo), by Cátedra Infoglobal-UAM for “Nuevas Tecnologías de video aplicadas a la seguridad”, by the Consejería de Educación of the Comunidad de Madrid and by the European Social Fund

Intelligent Traffic Signal Automation Based on Computer Vision Techniques Using Deep Learning

Traffic congestion in highly populated urban areas is a huge problem these days. A lot of researchers have proposed many systems to monitor traffic flow and handle congestion through different techniques. But the current systems are not reliable enough to perceive traffic signals in real-time. Therefore, we aim to build a system that can efficiently perform real-time environments to solve the traffic congestion problem through signal automation. Since vehicle detection and counting are crucial in any traffic system, we use state-of-the-art deep learning techniques to detect and count vehicles in real-time. We then automate the signal timings by comparing the count of traffic on all sides of a junction. These automated signal timings sufficiently reduce congestion and improve traffic flow. We prepared a dataset of 4500 images and achieved about 91% accuracy by training it on Faster RCNN

Background Subtraction in Video Surveillance

The aim of thesis is the real-time detection of moving and unconstrained surveillance environments monitored with static cameras. This is achieved based on the results provided by background subtraction. For this task, Gaussian Mixture Models (GMMs) and Kernel density estimation (KDE) are used. A thorough review of state-of-the-art formulations for the use of GMMs and KDE in the task of background subtraction reveals some further development opportunities, which are tackled in a novel GMM-based approach incorporating a variance controlling scheme. The proposed approach method is for parametric and non-parametric and gives us the better method for background subtraction, with more accuracy and easier parametrization of the models, for different environments. It also converges to more accurate models of the scenes. The detection of moving objects is achieved by using the results of background subtraction. For the detection of new static objects, two background models, learning at different rates, are used. This allows for a multi-class pixel classification, which follows the temporality of the changes detected by means of background subtraction. In a first approach, the subtraction of background models is done for parametric model and their results are shown. The second approach is for non-parametric models, where background subtraction is done using KDE non-parametric model. Furthermore, we have done some video engineering, where the background subtraction algorithm was employed so that, the background from one video and the foreground from another video are merged to form a new video. By doing this way, we can also do more complex video engineering with multiple videos. Finally, the results provided by region analysis can be used to improve the quality of the background models, therefore, considerably improving the detection results