scholarly journals Object Tracking by Corrected Background-Weighted Histogram Mean Shift with Sum of Gradient Mode

2013 ◽  
Author(s):  
Yu Yang ◽  
Yongxing Jia ◽  
Chuanzhen Rong ◽  
Lijuan Wang ◽  
Yuan Wang ◽  
...  
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Weighted Histogram ◽  
Gradient Mode

Object Tracking by Corrected Background-Weighted Histogram Mean Shift with Sum of Gradient Mode

2013 ◽  
Vol 347-350 ◽  
pp. 3774-3779
Author(s):  
Yu Yang ◽  
Yong Xing Jia ◽  
Chuan Zhen Rong ◽  
Li Juan Wang ◽  
Yuan Wang ◽  
...  
Keyword(s):  
Object Tracking ◽  
Iterative Procedure ◽  
Mean Shift ◽  
Target Localization ◽  
Experimental Results ◽  
Complex Environments ◽  
Algorithm Performance ◽  
Background Clutter ◽  
Weighted Histogram ◽  
Gradient Mode

With a focus on complex environments, the present paper describes a new algorithm in scale changed object tracking through color feature. Mean shift (MS) iterative procedure is the best color-based algorithm to find the location of an object. The algorithm performance is not acceptable once tracking scale changed objects in complex environments. In this paper, the main aim is to improve the MS method, using corrected background-weighted histogram (CBWH) algorithm to reduce the interference of background in target localization. To fit the object scale change, the sum of gradient mode (SGM) is employed. The experimental results show that the proposed method is superior to the traditional mean shift tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not influenced by the tracked objects scale changes; 3) it is less prone to the background clutter.

Corrected Background-weighted Histogram Mean Shift Tracking Algorithm Based on Adaptive Bandwidth

2013 ◽  
Vol 401-403 ◽  
pp. 1543-1546
Author(s):  
Feng Liu ◽  
Chao Zhang ◽  
Xiao Pei Wu
Keyword(s):  
Probability Distribution ◽  
Object Tracking ◽  
Model Updating ◽  
Mean Shift ◽  
Spatial Localization ◽  
Target Localization ◽  
Invariant Moment ◽  
Adaptive Kernel ◽  
Weighted Model ◽  
Weighted Histogram

The CBWH (corrected background-weighted histogram) scheme can effectively reduce backgrounds interference in target localization. But it still has the problem of scale and spatial localization inaccuracy. To solve the above issues, we proposed a method which generates a color probability distribution by taking advantage of the targets salient features. In the binary image, we calculate the invariant moment and thus to resize the tracking window of the next frame. A simple background-weighted model updating method is adopted to adapt to the complex background in tracking. Experimental results show that the proposed algorithm improves the robustness of object tracking by self-adaptive kernel-bandwidth updating.

Object Tracking Based on Corrected Background-Weighted Histogram Mean Shift and Kalman Filter

2013 ◽  
Vol 765-767 ◽  
pp. 720-725 ◽  
Author(s):  
Yu Yang ◽  
Yong Xing Jia ◽  
Chuan Zhen Rong ◽  
Ying Zhu ◽  
Yuan Wang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Complex Environments ◽  
The Real ◽  
Real Environment ◽  
Background Clutter ◽  
Weighted Histogram

The classical mean shift (MS) algorithm is the best color-based method for object tracking. However, in the real environment it presents some limitations, especially under the presence of noise, objects with partial and full occlusions in complex environments. In order to deal with these problems, this paper proposes a reliable object tracking algorithm using corrected background-weighted histogram (CBWH) and the Kalman filter (KF) based on the MS method. The experimental results show that the proposed method is superior to the traditional MS tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not influenced by the objects with partial and full occlusions; 3) it is less prone to the background clutter.

Implementasi Object Tracking Berbasis Filtering Warna Pada Sensor Kamera Pixy CMUcam 5

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Muh. Rezki Kurniwan
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Back Projection

Pelacakan benda bergerak atau object tracking merupakan suatu proses mengikuti posisi obyek di dalam suatu citra. Algoritma CamShift adalah singkatan dari Continuously Adaptive Meanshift, yang merupakan pengembangan dari algoritma Mean Shift yang dilakukan secara terus menerus (berulang) untuk melakukan adaptasi atau penyesuaian terhadap distribusi probabilitas warna yang selalu berubah tiap pergantian frame dari video. CamShift dapat melacak objek berwarna, dibutuhkan gambar distribusi probabilitas. Gambar-gambar menggunakan sistem warna HSV dan hanya menggunakan komponen Hue untuk membuat histogram warna objek 1D. Histogram ini disimpan untuk mengonversi  frame berikutnya yang cocok dengan probabilitas objek. Gambar distribusi probabilitas itu sendiri dibuat dengan melakukan back projection histogram hue 1D ke image hue pada frame. Hasilnya disebut gambar backproject. CamShift kemudian digunakan untuk melacak objek berdasarkan gambar backproject tersebut. Kata kunci :CamShift.Tracking. Objek

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