Vehicle Detection and Motion Analysis in Low-Altitude Airborne Video Under Urban Environment

Cao, Xianbin; Wu, Changxia; Lan, Jinhe; Yan, Pingkun; Li, Xuelong

doi:10.1109/tcsvt.2011.2162274

Cited by 81 publications

(52 citation statements)

References 26 publications

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“…The evaluation procedure uses confusion matrix then calculates the accuracy, specificity, sensitivity, and precision using (10), (11), (12), and (13) respectively where TP is true positive, FP is false positive, TN is true negative, and FN is false negative. BAC (Balance Accuracy) which calculated using (14) is also used for evaluation because of the imbalance between positive and negative test data [27].…”

Section: E Performance Evaluationmentioning

confidence: 99%

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Vehicle pose estimation for vehicle detection and tracking based on road direction

Prahara

Azhari

Murinto

2017

Int. J. Adv. Intell. Informatics

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Vehicle has several types and each of them has different color, size, and shape. The appearance of vehicle also changes if viewed from different viewpoint of traffic surveillance camera. This situation can create many possibilities of vehicle poses. However, the one in common, vehicle pose usually follows road direction. Therefore, this research proposes a method to estimate the pose of vehicle for vehicle detection and tracking based on road direction. Vehicle training data are generated from 3D vehicle models in four-pair orientation categories. Histogram of Oriented Gradients (HOG) and Linear-Support Vector Machine (Linear-SVM) are used to build vehicle detectors from the data. Road area is extracted from traffic surveillance image to localize the detection area. The pose of vehicle which estimated based on road direction will be used to select a suitable vehicle detector for vehicle detection process. To obtain the final vehicle object, vehicle line checking method is applied to the vehicle detection result. Finally, vehicle tracking is performed to give label on each vehicle. The test conducted on various viewpoints of traffic surveillance camera shows that the method effectively detects and tracks vehicle by estimating the pose of vehicle. Performance evaluation of the proposed method shows 0.9170 of accuracy and 0.9161 of balance accuracy (BAC).

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Section: E Performance Evaluationmentioning

confidence: 99%

“…Cao et al in their research proposed a method to detect and to analyze vehicle on low attitude traffic video [12]. Video data were extracted using Light-Boost Pyramid Sampling Histogram of Oriented Gradients (LBPS-HOG), which was a variant of HOG.…”

mentioning

confidence: 99%

Vehicle pose estimation for vehicle detection and tracking based on road direction

Prahara

Azhari

Murinto

2017

Int. J. Adv. Intell. Informatics

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“…Because of the low altitude, the resolution of targets is high, making the tracking problem much easier. Most of the challenge in tracking in these data sets is in compensating for the aircraft motion [3] and detecting targets of interest [4]. Once these problems are addressed, the actual tracking is straightforward and can be done with generic object trackers, such as the kernel-based object tracking method of Comaniciu et al [5].…”

Section: A Related Workmentioning

confidence: 99%

Tracking Vehicles Through Shadows and Occlusions in Wide-Area Aerial Video

Aeschliman

Park

Kak

2014

IEEE Trans. Aerosp. Electron. Syst.

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We present a new approach for simultaneous tracking and segmentation of multiple targets in low frame rate aerial video. We focus on building an accurate background model that accounts for both global camera motion and moving objects in the scene. We then apply a probabilistic framework for simultaneous tracking and segmentation that incorporates this background model. By using a background model, we are able to track the object through dramatic appearance changes caused by shadows and lighting changes. Furthermore, the incorporation of segmentation into the tracking algorithm reduces the impact of common tracking problems, such as drift and partial occlusion. Results are shown for the Columbus Large Image Format (CLIF) 2007 data set, demonstrating successful tracking under significant occlusions, target appearance changes, and near similar moving objects.

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“…Xianbin Cao, Changxia Wu et al [4] proposed an extension of SVM classifier added new features of HOG overcoming the disadvantages that are called boosting light and pyramid sampling histogram of oriented gradients (bLPS-HOG). This classifier is used to detect vehicle in low height airborne videos.…”

Section: Vehicle Detection Motion Analysis In Low Altitude Airborne Vmentioning

confidence: 99%

Comparative Study on Vehicle Detection Techniques in Aerial Surveillance

Selvakumar¹,

Anbu²

2013

IJCI

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Vehicle Detection and Motion Analysis in Low-Altitude Airborne Video Under Urban Environment

Cited by 81 publications

References 26 publications

Vehicle pose estimation for vehicle detection and tracking based on road direction

Vehicle pose estimation for vehicle detection and tracking based on road direction

Tracking Vehicles Through Shadows and Occlusions in Wide-Area Aerial Video

Comparative Study on Vehicle Detection Techniques in Aerial Surveillance

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