Real‐time vehicle detection with foreground‐based cascade classifier

Zhuang, Xiaobin; Kang, Wenxiong; Wu, Qiuxia

doi:10.1049/iet-ipr.2015.0333

Cited by 31 publications

(26 citation statements)

References 21 publications

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“…Haar-SURF features were then added with the AdaBoost classifier for object detection, giving better performance [10], overcoming weaknesses in the processing of foreground-based Haar features. Cascaded classifiers with Haar features were added to improve this feature [11] [8]. The traditional AdaBoost method was again enhanced with the added feature of multi-scan detection techniques with soft cascaded classifiers [16].…”

Section: Related Workmentioning

confidence: 99%

Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning

et al. 2020

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In this paper, specifically for detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared. One is a machine learning approach, known as 'Cascaded Object Detector' and the other is a composition of traditional customized methods, individually known as 'Colour Transformation', 'Colour Segmentation' and 'Circular Hough Transformation'. The (Viola Jones) Cascaded Object Detector generates 'histogram of oriented gradient' (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, color thresholding is applied to detect tomatoes either from a natural or solid background and RGB colour is adjusted to identify ripened tomatoes. In this work, Colour Segmentation is applied in the detection of tomatoes with defects, which has not previously been applied under machine learning techniques. The function modules of this algorithm are fed formatted images, captured by a camera mounted on a mobile robot. This robot was designed, built and operated in a tomato field to identify and quantify both green and ripened tomatoes as well as to detect damaged/blemished ones. This algorithm is shown to be optimally feasible for any micro-controller based miniature electronic devices in terms of its run time complexity of O(n 3) for traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB. This result is potentially significant for farmers in crop fields to identify the condition of tomatoes quickly.

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Section: Related Workmentioning

confidence: 99%

Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning

et al. 2020

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“…Enhancements such as utilizing GPU programming or increasing other features depend on the texture of the vehicles classifying portion, which is proposed to enhance the system run time and performance. Zhuang et al [16] suggested an algorithm for vehicle detection in real-time, which depends on the enhanced Haar-like features and gathering a cascade of classifiers with motion detection. It adapts a background extractor based on visual features, supplemented by a morphological process, to acquire a foreground.…”

Section: Najm and Alimentioning

confidence: 99%

Automatic Vehicles Detection, Classification and Counting Techniques / Survey

Najm

Ali

2020

eijs

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Vehicle detection (VD) plays a very essential role in Intelligent Transportation Systems (ITS) that have been intensively studied within the past years. The need for intelligent facilities expanded because the total number of vehicles is increasing rapidly in urban zones. Trafﬁc monitoring is an important element in the intelligent transportation system, which involves the detection, classification, tracking, and counting of vehicles. One of the key advantages of traffic video detection is that it provides traffic supervisors with the means to decrease congestion and improve highway planning. Vehicle detection in videos combines image processing in real-time with computerized pattern recognition in flexible stages. The real-time processing is very critical to keep the appropriate functionality of automated or continuously working systems. VD in road traffics has numerous applications in the transportation engineering field. In this review, different automated VD systems have been surveyed, with a focus on systems where the rectilinear stationary camera is positioned above intersections in the road rather than being mounted on the vehicle. Generally, three steps are utilized to acquire traffic condition information, including background subtraction (BS), vehicle detection and vehicle counting. First, we illustrate the concept of vehicle detection and discuss background subtraction for acquiring only moving objects. Then a variety of algorithms and techniques developed to detect vehicles are discussed beside illustrating their advantages and limitations. Finally, some limitations shared between the systems are demonstrated, such as the definition of ROI, focusing on only one aspect of detection, and the variation of accuracy with quality of videos. At the point when one can detect and classify vehicles, then it is probable to more improve the flow of the traffic and even give enormous information that can be valuable for many applications in the future.

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“…The detection algorithm used cascade classifiers which was trained by Gentle AdaBoost classifier with Haar-SURF mixed features. Afterwards, in 2016 the detection got improved with foreground based Haar like features and cascade classifiers [13]. The algorithm was successfully evaluated and implemented with public datasets.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of a surveillance system to detect and track vehicles using Haar cascaded classifiers and optical flow method

Noor

Siddiquee

Sarma

et al. 2017

2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA)

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This paper presents the real time vehicle detection and tracking system, based on data, collected from a single camera. In this system, vehicles are detected by using Haar Feature-based Cascaded Classifier on static images, extracted from the video file. The advantage of this classifier is that, it uses floating numbers in computations and hence, 20% more accuracy can be achieved in comparison to other classifiers and features of classifiers such as LBP (Local Binary Pattern). Tracking of the vehicles is carried out using Lucas-Kanade and Horn Schunk Optical Flow method because it performs better than other methods such as Morphological and Correlation Transformations. The proposed system consists of vehicle detection and tracking; and it is evaluated by using real data, collected from the route networks of Chittagong City of Bangladesh.

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Real‐time vehicle detection with foreground‐based cascade classifier

Cited by 31 publications

References 21 publications

Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning

Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning

Automatic Vehicles Detection, Classification and Counting Techniques / Survey

Performance analysis of a surveillance system to detect and track vehicles using Haar cascaded classifiers and optical flow method

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