KLT Feature Based Vehicle Detection and Tracking in Airborne Videos

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

doi:10.1109/icig.2011.92

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…Recently, a method based on KLT feature was developed to track moving object. 3,4 However, this approach cannot achieve satisfactory performances for a moving camera. Another method based on HMM (Hidden Markov Model) was to detect object via color and optical flow information.…”

Section: Introductionmentioning

confidence: 97%

Object detection and tracking with active camera on motion vectors of feature points and particle filter

Chen

Shang

2013

Review of Scientific Instruments

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A method based on motion vectors of feature points and particle filter has been proposed and developed for an active∕moving camera for object detection and tracking purposes. The object is detected by histogram of motion vectors first, and then, on the basis of particle filter algorithm, the weighing factors are obtained via color information. In addition, re-sampling strategy and surf feature points are used to remedy the drawback of particle degeneration. Experimental results demonstrate the practicability and accuracy of the new method and are presented in the paper.

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Section: Introductionmentioning

confidence: 97%

Object detection and tracking with active camera on motion vectors of feature points and particle filter

Chen

Shang

2013

Review of Scientific Instruments

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“…Furthermore, due to parallax effects and misalignment during image registration, distinguishing between motion and noise in the difference image becomes even more difficult [22]. Just like few authors [3,14,22], we propose to use clustering of moving local features such as KLT features or Harris corners to determine motion areas. However, undersegmentation for vehicles driving one behind the other and oversegmentation for weakly textured vehicles as both seen in Fig.…”

Section: Introductionmentioning

confidence: 98%

Evaluation of object segmentation to improve moving vehicle detection in aerial videos

Teutsch

Krüger

Beyerer

2014

2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Moving objects play a key role for gaining scene understanding in aerial surveillance tasks. The detection of moving vehicles can be challenging due to high object distance, simultaneous object and camera motion, shadows, or weak contrast. In scenarios where vehicles are driving on busy urban streets, this is even more challenging due to possible merged detections. In this paper, a video processing chain is proposed for moving vehicle detection and segmentation. The fundament for detecting motion which is independent of the camera motion is tracking of local image features such as Harris corners. Independently moving features are clustered. Since motion clusters are prone to merge similarly moving objects, we evaluate various object segmentation approaches based on contour extraction, blob extraction, or machine learning to handle such effects. We propose to use a local sliding window approach with Integral Channel Features (ICF) and AdaBoost classifier

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“…Feature Point Detection, Matching and Tracking is an important problem that has been studied extensively in the Computer Vision literature and has numerous applications such as Mosaicing, Object Tracking [33,35,8], Action Recognition [37,17,18,38] and Structure-from-Motion [1,5,25] among others. The Kanade-Lukas-Tomasi (KLT) [15,36,30] tracker is still the most widely used tracker in the literature even after 30 years due to its robustness and speed.…”

Section: Introductionmentioning

confidence: 99%

CoMaL Tracking: Tracking Points at the Object Boundaries

Ramakrishnan

Ravindran

Mittal

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Traditional point tracking algorithms such as the KLT use local 2D information aggregation for feature detection and tracking, due to which their performance degrades at the object boundaries that separate multiple objects. Recently, CoMaL Features have been proposed that handle such a case. However, they proposed a simple tracking framework where the points are re-detected in each frame and matched. This is inefficient and may also lose many points that are not re-detected in the next frame. We propose a novel tracking algorithm to accurately and efficiently track CoMaL points. For this, the level line segment associated with the CoMaL points is matched to MSER segments in the next frame using shape-based matching and the matches are further filtered using texture-based matching. Experiments show improvements over a simple redetect-and-match framework as well as KLT in terms of speed/accuracy on different real-world applications, especially at the object boundaries.

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KLT Feature Based Vehicle Detection and Tracking in Airborne Videos

Cited by 11 publications

References 8 publications

Object detection and tracking with active camera on motion vectors of feature points and particle filter

Object detection and tracking with active camera on motion vectors of feature points and particle filter

Evaluation of object segmentation to improve moving vehicle detection in aerial videos

CoMaL Tracking: Tracking Points at the Object Boundaries

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