Modified codebook algorithm with Kalman filter for foreground segmentation in video sequences

2017 ◽  
Author(s):  
Su Su Aung ◽  
Zin Mar Kyu
Keyword(s):  
Kalman Filter ◽  
Video Sequences ◽  
Foreground Segmentation

Foreground Segmentation Improvement by Image Denoising Preprocessing Applied to Noisy Video Sequences

2021 ◽  
pp. 388-397
Author(s):  
Jorge García-Gozález ◽  
Juan Miguel Ortiz-de-Lazcano-Lobato ◽  
Rafael Marcos Luque-Baena ◽  
Ezequiel López-Rubio
Keyword(s):  
Image Denoising ◽  
Video Sequences ◽  
Foreground Segmentation

Robust Inland Waterway Ship Tracking via Hybrid TLD and Kalman Filter

2014 ◽  
Vol 1037 ◽  
pp. 373-377 ◽  
Author(s):  
Teng Fei ◽  
Liu Qing ◽  
Lin Zhu ◽  
Jing Li
Keyword(s):  
Kalman Filter ◽  
Visual Tracking ◽  
State Of The Art ◽  
Experimental Results ◽  
Closed Circuit ◽  
Video Sequences ◽  
Inland Waterway ◽  
Cluttered Background ◽  
Full Occlusion

In this paper, we mainly address the problem of tracking a single ship in inland waterway CCTV (Closed-Circuit Television) video sequences. Although state-of-the-art performance has been demonstrated in TLD (Tracking-Learning-Detection) visual tracking, it is still challenging to perform long-term robust ship tracking due to factors such as cluttered background, scale change, partial or full occlusion and so forth. In this work, we focus on tracking a single ship when it suffers occlusion. To accomplish this goal, an effective Kalman filter is adopted to construct a novel online model to adapt to the rapid ship appearance change caused by occlusion. Experimental results on numerous inland waterway CCTV video sequences demonstrate that the proposed algorithm outperforms the original one.

scholarly journals A Hybrid Motion Estimation for Video Stabilization Based on an IMU Sensor

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2708 ◽  
Author(s):  
Jutamanee Auysakul ◽  
He Xu ◽  
Vishwanath Pooneeth
Keyword(s):  
Kalman Filter ◽  
Poor Performance ◽  
Video Data ◽  
Frame Rate ◽  
Video Sequences ◽  
Video Stabilization ◽  
Switching Function ◽  
Post Processing ◽  
The Poor ◽  
Hybrid Motion

Recorded video data must be clear for accuracy and faster analysis during post-processing, which often requires video stabilization systems to remove undesired motion. In this paper, we proposed a hybrid method to estimate the motion and to stabilize videos by the switching function. This method switched the estimated motion between a Kanade–Lucus–Tomasi (KLT) tracker and an IMU-aided motion estimator. It facilitated the best function to stabilize the video in real-time as those methods had numerous advantages in estimating the motion. To achieve this, we used a KLT tracker to correct the motion for low rotations and an IMU-aided motion estimator for high rotation, owing to the poor performance of the KLT tracker during larger movements. Furthermore, a Kalman filter was used to remove the undesired motion and hence smoothen the trajectory. To increase the frame rate, a multi-threaded approach was applied to execute the algorithm in the array. Irrespective of the situations exposed to the experimental results of the moving camera from five video sequences revealed that the proposed algorithm stabilized the video efficiently.

An Adaptive Tracking Algorithm Based on Mean Shift

2012 ◽  
Vol 538-541 ◽  
pp. 2607-2613 ◽  
Author(s):  
Zheng Hong Deng ◽  
Ting Ting Li ◽  
Ting Ting Zhang
Keyword(s):  
Kalman Filter ◽  
Mean Shift ◽  
Initial Point ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Statistical Probability ◽  
Mean Shift Algorithm ◽  
Target Model ◽  
The Mean ◽  
Selective Mechanism

Object tracking is to search the most similar parts to targets in video sequences. Among the various tracking algorithms, mean shift tracking algorithm has become popular due to its simplicity, efficiency and good performance. This paper focused on mean shift tracking algorithm, which is a modeling mechanism based on statistical probability density function. In practice, when the background of the tracking and characteristics of the target are similar, pixels of background occupy a large proportion in the histogram. The traditional mean shift cannot adapt to the mutative scene. Meanwhile, if there is block or disappearance, the result is not exact. Three algorithms were given for above difficulties. A weighted template background was established, that can highlight the features of target and improve real-time. Then this paper presented a selective mechanism to update the target model. Every component is updated based on the contribution to the target model. Finally, the Kalman filter was combined with mean shift algorithm. We saw the prediction points of Kalman filter as the initial point, carried out the mean shift iteration and then updated Kalman filter using the ultimate location. Extensive experimental results illustrated excellent agreement with these methods.

Online Pedestrian Tracking with Kalman Filter and Random Ferns

2014 ◽  
Vol 536-537 ◽  
pp. 205-212
Author(s):  
Zhi Qiang Qu ◽  
Dan Tu ◽  
Jun Lei
Keyword(s):  
Computer Vision ◽  
Kalman Filter ◽  
Object Tracking ◽  
Experimental Results ◽  
Video Sequences ◽  
Pedestrian Tracking ◽  
Excellent Performance ◽  
Tracking Process ◽  
Shape Changes ◽  
Made In

Object tracking is one of the most important components in numerous applications of computer vision. Much progress has been made in recent years. The Tracking-Learning-Detection (TLD) algorithm achieves excellent performance on a set of challenging video sequences. However, classical TLD algorithm fails to track non-rigid pedestrian as complicated appearance, varying viewpoints, shape changes and occlusions. In this paper, we follow the TLD and propose a robust KMD framework which consists of the Kalman filter tracker, random ferns pedestrian detector and the pedestrian model. During the tracking process the tracker and detector are complementary: the Kalman filter tracker predicts the motion, the pedestrian detector searches the best-match appearance and the pedestrian model combines performance of both to determine final result and generate new samples for learning. Experimental results show our framework comparatively improves performance for pedestrian tracking in surveillance videos.

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