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This paper proposes optimal color space based probabilistic foreground detector. The intuition is to employ two most widely used color spaces (RGB and YCbCr) one at a time to model background. A decision criteria to select optimal color space is based on mean squared error (MSE). Initial frames (say 100) without any foreground information are used to compute MSE for both color spaces. Color space...
This paper proposes optimal background modeling scheme for the cluttered scenes. The background initialization is the first step in the process of segmenting out moving information. Concrete background model ensures the proper segmentation of moving information from the scene. Each pixel is modeled as mixture of Gaussian. Using decision criteria, background/foreground pixels are differentiated. During...
The lane marking detection task is an essential process in the field of semi-autonomous and autonomous navigation. This paper proposes a method that combines the color and edge information to robustly detect the lane marking within the image either located far on near to the vehicle. Firstly, the region of interest is extracted from the image. Secondly, the set of lane marking features are extracted...
This paper proposes efficient real time method for sterile zone monitoring with human verification. The propose method consists of two main parts: Motion detection module and human verification module. The role of motion detection module is to segment out foreground object from background. Probabilistic Foreground Detector based on Gaussian Mixture Model(GMM) is used. Region of interest (ROI) obtained...
Foreground detection is one of the well and widely studied research topic in the field of computer vision. However, it still fails to cope with the many practical issues such as illumination changes, dynamic backgrounds, and shadow. This paper proposes optimal color space based probabilistic foreground detector. The intuition is to employ two most widely used color spaces (RGB and YCbCr) one at a...
This paper proposes a coarse-to-fine approach for fast image tracking. The tracking method is built based on correlation tracker which employs online learning and fast detection by utilizing Fourier transform principles. Firstly, a small patch is extracted from a region near the tracked pixel. This patch is divided into a number of cells and then features are extracted from each cell, providing a...
Foreground detection can be considered as backbone of multistage computer vision systems. Foreground detection using Gaussian Mixture Models (GMM) is famous choice because of its good accuracy and low computational cost. There are several parameters (e.g., learning rate, mean, and variance) involved in the model and assigning appropriate values may lead to better foreground segmentation. This paper...
This paper introduces a method for analyzing the critical situation based on collision risk probability. Pedestrians in the scene are captured from a monocular camera mounted on the vehicle. Position information of object is extracted by projecting the centroid of bounding box to the ground plane. Five elements of collision criteria are used for our risk analysis. Pedestrian walking direction, its...
Foreground detection is the classical computer vision task of segmenting out motion information from a particular scene. Foreground detection using Gaussian Mixture Models (GMM) is the famous choice. Since first time proposed, many researchers tried to improve GMM. This paper focuses on the comparative evaluation of three most famous improvements in the algorithm. The improved methods are compared...
This paper proposes a method for detecting moving objects appeared in video captured by a moving camera. The proposed method relies on dense optical flow to differentiate moving objects from static background. Whenever video taken from a static camera is used, the dense optical flow itself is sufficient to determine the moving object in the scenes. However, in a non-static camera, all pixels are moving...
This paper proposes probabilistic foreground detector based on Gaussian Mixture Models (GMM) for sterile zone monitoring at night time. The foreground object may exhibit camouflage effect due to the thermal camera. GMM is prone to camouflage effect. The proposed system consists of three modules namely pre-processing, foreground detection, and post processing. Firstly, every frame is enhanced using...
This paper presents development of vision-based intelligent surveillance system which covers multiple tasks, such as abandoned object detection, fire and smoke detection, human detection and tracking, sterile zone monitoring, and illegally parked vehicle detection. Our goal is combining all these tasks into integrated system considering not only high accuracy, but also fast processing time.
Probabilistic modeling of background is extensively used for foreground detection in computer vision. Gaussian Mixture Models (GMM) is famous choice for detecting foreground in video sequences owing to ability of adapting background variation. However, GMM is prone to camouflage effect i.e. foreground object and background having same pixel intensity. This paper proposes foreground detector based...
A collision risk estimation plays a crucial role in both driver and pedestrian safety in advanced driver assistance systems (ADAS) and autonomous vehicle navigation(AVN). In the proposed approach, an object warning collision system is implemented using a laser sensor. We focus on high conflict vehicle/pedestrian zones within a range of [20km/h, 30km/h]. The proposed method was implemented in four...
This paper explains the way of unification of flame and smoke detection algorithms by merging the common steps into a single processing flow. Scenario, discussed in the current manuscript, considers using fixed surveillance cameras that allows using background subtraction to detect changes in a scene. Due to imperfection of background subtraction, foreground pixels, belonging to the same real object,...
This paper presents a method for estimating of walking direction for pedestrian path prediction. Pedestrian intending to laterally cross the street is observed by images which captured from a monocular camera mounted on the vehicle. The positional information of object is obtained by projecting the centroid of bounding box in the ground plane. Then, dependency between the real worlds, global coordinates...
Detection of a moving object is often considered first step of multistage computer vision system such as visual surveillance. This paper proposes foreground detector based on Gaussian Mixture Models (GMM) for sterile zone monitoring. Each pixel is modeled by a mixture of Gaussians. Additionally, Morphological operations are incorporated on a foreground mask to reduce undesirable noise, thereby, restoring...
This paper presents a comparative study of several state of the art background subtraction (BS) algorithms. The goal is to provide brief solid overview of the strengths and weaknesses of the most widely applied BS methods. Approaches ranging from simple background subtraction with global thresholding to more sophisticated statistical methods have been implemented and tested with ground truth. The...
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