Vehicle target detection methods based on color fusion deformable part model

EURASIP Journal on Wireless Communications and Networking, May 2018

In this paper, the traditional vehicle target detection method is improved, and a vehicle target detection method based on color fusion deformable part model (DPM) is proposed. Firstly, the traffic image is conducted with HSI color space conversion, and then, the information of each channel in color space is extracted, the DPM of each channel is trained, and then the color fusion DPM is obtained by using the adaptive fusion method. In the process of vehicle detection, traversal search of vehicle images is conducted using color fusion DPM through the sliding window traversal; areas with a score exceeding the threshold are deemed as the vehicle targets. In the experimental phase, we first trained the color fusion DPM, then validated the validity and accuracy, and the experiment images were from the practical traffic junctions. The results show that the method proposed in this paper can carry out vehicle target detection accurately and effectively. Compared with other vehicle target detection methods, it has high detection rate and low false positive rate, which can achieve the accurate detection of vehicle targets in intelligent transportation.

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Vehicle target detection methods based on color fusion deformable part model

Zhang EURASIP Journal on Wireless Communications and Networking Vehicle target detection methods based on color fusion deformable part model Dongbing Zhang 0 0 School of Computer Science and Technology/Information College, Huaibei Normal University , Huaibei 235000 , China In this paper, the traditional vehicle target detection method is improved, and a vehicle target detection method based on color fusion deformable part model (DPM) is proposed. Firstly, the traffic image is conducted with HSI color space conversion, and then, the information of each channel in color space is extracted, the DPM of each channel is trained, and then the color fusion DPM is obtained by using the adaptive fusion method. In the process of vehicle detection, traversal search of vehicle images is conducted using color fusion DPM through the sliding window traversal; areas with a score exceeding the threshold are deemed as the vehicle targets. In the experimental phase, we first trained the color fusion DPM, then validated the validity and accuracy, and the experiment images were from the practical traffic junctions. The results show that the method proposed in this paper can carry out vehicle target detection accurately and effectively. Compared with other vehicle target detection methods, it has high detection rate and low false positive rate, which can achieve the accurate detection of vehicle targets in intelligent transportation. Color fusion DPM; Vehicle detection; HSI; Intelligent transportation 1 Introduction With the rapid development of intelligent transportation system, vehicle target detection has become a popular research field as it is an important part of modern intelligent transportation system. The traditional vehicle detection method is to install the induction coil on the road to collect the vehicle images. The disadvantage of this method is that the road is damaged and the installation and maintenance of the system are inconvenient. With the development of image processing technology, more and more vehicle detection algorithms based on computer vision and image processing technology have been widely used [ 1 ]. At present, the detection of vehicle target is based on the existence of motion information, which is classified into two categories. One is vehicle detection using motion information, such as inter-frame difference method [ 2 ], background difference method [ 3 ], and optical flow method [ 4, 5 ]. These typical methods rely on the vehicle’s motion information and the detection algorithm fails when the information is lost. The other is vehicle detection that does not rely on motion information, which usually starts with the characteristics of the vehicle itself. According to the vehicle shape and posture, many scholars have proposed a method based on modeling and template matching [ 6, 7 ]. This kind of method usually builds the vehicle model first and then matches the model with the test image to get the vehicle target. This method has high requirements on the model and is susceptible to noise when the model is not consistent with the actual situation. According to the appearance characteristics of the vehicle such as color and texture, many scholars have proposed a method based on these features [ 8, 9 ]. This kind of method usually studies the appearance difference of vehicle and non-vehicle in color, texture, etc. This method allows for better detection of typical vehicle targets but can easily undetect vehicles that are close to ground color and texture. Relying on new technologies such as big data, some scholars have proposed methods based on statistics and machine learning [ 10–12 ]. This kind of methods adopt the idea of statistical analysis, extract the features suitable for vehicle detection, and adopt neural network and support vector machine and other methods to carry out learning and testing. The methods feature a high accuracy but require a large number of samples for classifier learning; performance also differs among different classifiers, and a large amount of computation is needed. These methods have played a crucial role in promoting vehicle target detection. Based on the above methods, this paper proposes a vehicle target detection method based on color fusion DPM, which takes into account the vehicle’s gradient information and color information while modeling the vehicle, and can effectively and accurately realize the vehicle target detection. 2 Vehicle detection method based on color fusion DPM 2.1 DPM detection principle Deformable part model (DPM) [ 13–15 ] is a target detection classification method featuring high efficiency and high precision. DPM first establishes a model for the detection target; the model is divided into a root model and a part model to make the target shape and posture more robust. The root model describes the overall characteristics of the target, and the part model describes the local detail features of the target; the relationship (...truncated)


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Dongbing Zhang. Vehicle target detection methods based on color fusion deformable part model, EURASIP Journal on Wireless Communications and Networking, 2018, pp. 94, Volume 2018, Issue 1, DOI: 10.1186/s13638-018-1111-8