A Gaussian mixture based optical flow modeling for object detection

Detection of a vehicle is a very important aspect for traffic monitoring. It is based on the concept of moving object detection. Classifying the detected object as vehicle and class of vehicle is also having application in various application domains. This paper aims at providing an application of vehicle detection and classification concept to detect vehicles along curved roads in Indian scenarios. The main purpose is to ensure safety in such roads. Gaussian mixture model and blob analysis are the methods applied for the detection of vehicles. Morphological operations are used to eliminate noise. The moving vehicles are detected and the class of the vehicle is identified.

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MULTI OBJECT DETECTION AND TRACKING USING OPTICAL FLOW DENSITY – HUNGARIAN KALMAN FILTER (OFD - HKF) ALGORITHM FOR VEHICLE COUNTING

Jurnal Ilmu Komputer dan Informasi ◽

10.21609/jiki.v11i1.581 ◽

2018 ◽

Vol 11 (1) ◽

pp. 17 ◽

Cited By ~ 2

Author(s):

Muhamad Soleh ◽

Grafika Jati ◽

Muhammad Hafizhuddin Hilman

Keyword(s):

Kalman Filter ◽

Object Detection ◽

Optical Flow ◽

Motion Blur ◽

Research Topic ◽

Vehicle Tracking ◽

Flow Density ◽

Illumination Change ◽

Dynamic Background ◽

Detection And Tracking

Intelligent Transportation Systems (ITS) is one of the most developing research topic along with growing advance technology and digital information. The benefits of research topic on ITS are to address some problems related to traffic conditions. Vehicle detection and tracking is one of the main step to realize the benefits of ITS. There are several problems related to vehicles detection and tracking. The appearance of shadow, illumination change, challenging weather, motion blur and dynamic background such a big challenges issue in vehicles detection and tracking. Vehicles detection in this paper using the Optical Flow Density algorithm by utilizing the gradient of object displacement on video frames. Gradient image feature and HSV color space on Optical flow density guarantee the object detection in illumination change and challenging weather for more robust accuracy. Hungarian Kalman filter algorithm used for vehicle tracking. Vehicle tracking used to solve miss detection problems caused by motion blur and dynamic background. Hungarian kalman filter combine the recursive state estimation and optimal solution assignment. The future positon estimation makes the vehicles detected although miss detection occurance on vehicles. Vehicles counting used single line counting after the vehicles pass that line. The average accuracy for each process of vehicles detection, tracking, and counting were 93.6%, 88.2% and 88.2% respectively.

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Deep-Learning-Based Road Crack Detection Frameworks for Dashcam-captured Images under Different Illumination Conditions

10.21203/rs.3.rs-685762/v1 ◽

2021 ◽

Author(s):

Da-Ren Chen ◽

Wei-Min Chiu

Keyword(s):

Object Detection ◽

Large Scale ◽

Crack Detection ◽

State Of The Art ◽

Gaussian Mixture Models ◽

Gaussian Mixture ◽

Machine Learning Techniques ◽

Detection Accuracy ◽

The Road ◽

Art Object

Abstract Machine learning techniques have been used to increase detection accuracy of cracks in road surfaces. Most studies failed to consider variable illumination conditions on the target of interest (ToI), and only focus on detecting the presence or absence of road cracks. This paper proposes a new road crack detection method, IlumiCrack, which integrates Gaussian mixture models (GMM) and object detection CNN models. This work provides the following contributions: 1) For the first time, a large-scale road crack image dataset with a range of illumination conditions (e.g., day and night) is prepared using a dashcam. 2) Based on GMM, experimental evaluations on 2 to 4 levels of brightness are conducted for optimal classification. 3) the IlumiCrack framework is used to integrate state-of-the-art object detecting methods with CNN to classify the road crack images into eight types with high accuracy. Experimental results show that IlumiCrack outperforms the state-of-the-art R-CNN object detection frameworks.

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Moving object detection based on optical flow and neural network fusion

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-06-2016-0020 ◽

2016 ◽

Vol 9 (4) ◽

pp. 325-335 ◽

Cited By ~ 3

Author(s):

Haiqun Qin ◽

Ziyang Zhen ◽

Kun Ma

Keyword(s):

Neural Network ◽

Object Detection ◽

Optical Flow ◽

Target Detection ◽

Moving Object Detection ◽

Moving Object ◽

Monitoring Systems ◽

Dynamic Background ◽

Content Type ◽

New Generation

Purpose The purpose of this paper is to meet the large demand for the new-generation intelligence monitoring systems that are used to detect targets within a dynamic background. Design/methodology/approach A dynamic target detection method based on the fusion of optical flow and neural network is proposed. Findings Simulation results verify the accuracy of the moving object detection based on optical flow and neural network fusion. The method eliminates the influence caused by the movement of the camera to detect the target and has the ability to extract a complete moving target. Practical implications It provides a powerful safeguard for target detection and targets the tracking application. Originality/value The proposed method represents the fusion of optical flow and neural network to detect the moving object, and it can be used in new-generation intelligent monitoring systems.

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