Online Boosting for Vehicle Detection

Dept. of Electr. Eng., Nat. Taipei Univ. of Technol., Taipei, Taiwan
IEEE TRANSACTIONS ON CYBERNETICS (Impact Factor: 6.22). 07/2010; 40(3):892 - 902. DOI: 10.1109/TSMCB.2009.2032527
Source: IEEE Xplore


This paper presents a real-time vision-based vehicle detection system employing an online boosting algorithm. It is an online AdaBoost approach for a cascade of strong classifiers instead of a single strong classifier. Most existing cascades of classifiers must be trained offline and cannot effectively be updated when online tuning is required. The idea is to develop a cascade of strong classifiers for vehicle detection that is capable of being online trained in response to changing traffic environments. To make the online algorithm tractable, the proposed system must efficiently tune parameters based on incoming images and up-to-date performance of each weak classifier. The proposed online boosting method can improve system adaptability and accuracy to deal with novel types of vehicles and unfamiliar environments, whereas existing offline methods rely much more on extensive training processes to reach comparable results and cannot further be updated online. Our approach has been successfully validated in real traffic environments by performing experiments with an onboard charge-coupled-device camera in a roadway vehicle.

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    • "Ensemble learning is a computational intelligence method, and theory and experiment have proved that the combination of the predictions of many individual detectors can enhance the generalization performance. There are many different ensemble learning methods used widely and successfully such as Bagging [16] [17], Boosting [18] [19], Random Forest [20], and their online version [21] [22]. Generally, an ensemble anomaly detector is constructed in two steps. "
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    ABSTRACT: In this paper, a novel distributed online anomaly detection method in resource-constrained WSNs was proposed. Firstly, the spatiotemporal correlation existing in the sensed data was exploited and a series of single anomaly detectors were built in each distributed deployment sensor node based on ensemble learning theory. Secondly, these trained detectors were broadcasted to the member sensor nodes in the cluster, combining with its trained detector, and the initial ensemble detector was built. Thirdly, considering resources-constrained WSNs, ensemble pruning based on biogeographical based optimization (BBO) was employed in the cluster head node to obtain an optimized subset of ensemble members. Further, the pruned ensemble detector coded by the state matrix was broadcasted to each member sensor nodes for the distributed online global anomaly detection. Finally, the experiments operated on a real WSN dataset demonstrated the effectiveness of the proposed method.
    Full-text · Article · Oct 2015 · International Journal of Distributed Sensor Networks
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    • "Chung et al [12] developed a real-time vision-based vehicle detection system that employs an online boosting algorithm. It is an online AdaBoost approach that cascades various strong classifiers instead of a single strong classifier. "

    Preview · Article · Jun 2015
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    • "Besides, the front obstacle detection was discussed enthusiastically in the past decade. Online boosting algorithm is proposed to detect the vehicle in front of the host car [2]. The online learning algorithm can conquer the online tuning problem for a practical system. "
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    ABSTRACT: This paper presents an effective vehicle and motorcycle detection system in the blind spot area in the daytime and nighttime scenes. The proposed method identifies vehicle and motorcycle by detecting the shadow and the edge features in the daytime, and the vehicle and motorcycle could be detected through locating the headlights at nighttime. First, shadow segmentation is performed to briefly locate the position of the vehicle. Then, the vertical and horizontal edges are utilized to verify the existence of the vehicle. After that, tracking procedure is operated to track the same vehicle in the consecutive frames. Finally, the driving behavior is judged by the trajectory. Second, the lamps in the nighttime are extracted based on automatic histogram thresholding, and are verified by spatial and temporal features to against the reflection of the pavement. The proposed real-time vision-based Blind Spot Safety-Assistance System has implemented and evaluated on a TI DM6437 platform to perform the vehicle detection on real highway, expressways, and urban roadways, and works well on sunny, cloudy, and rainy conditions in daytime and night time. Experimental results demonstrate that the proposed vehicle detection approach is effective and feasible in various environments.
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