<i>Multiple Object Tracking-by-Detection for Fruit Counting on an Apple Tree Canopy</i>

2018 ◽  
Keyword(s):  
Object Tracking ◽  
Apple Tree ◽  
Multiple Object Tracking ◽  
Tree Canopy ◽  
Multiple Object ◽  
Tracking By Detection

Multiple Object Tracking using YOLO-based Detector

2021 ◽  
Author(s):  
Shinfeng D. Lin ◽  
Tingyu Chang ◽  
Wensheng Chen
Keyword(s):  
Object Tracking ◽  
State Of The Art ◽  
Multiple Object Tracking ◽  
Motion Prediction ◽  
Multiple Object ◽  
Bounding Box ◽  
Video Frames ◽  
Tracking Process ◽  
Tracking By Detection ◽  
Bounding Boxes

In computer vision, multiple object tracking (MOT) plays a crucial role in solving many important issues. A common approach of MOT is tracking by detection. Tracking by detection includes occlusions, motion prediction, and object re-identification. From the video frames, a set of detections is extracted for leading the tracking process. These detections are usually associated together for assigning the same identifications to bounding boxes holding the same target. In this article, MOT using YOLO-based detector is proposed. The authors’ method includes object detection, bounding box regression, and bounding box association. First, the YOLOv3 is exploited to be an object detector. The bounding box regression and association is then utilized to forecast the object’s position. To justify their method, two open object tracking benchmarks, 2D MOT2015 and MOT16, were used. Experimental results demonstrate that our method is comparable to several state-of-the-art tracking methods, especially in the impressive results of MOT accuracy and correctly identified detections.

Multiple-Object Tracking

2011 ◽  
Vol 58 (3) ◽  
pp. 196-200 ◽  
Author(s):  
K. Botterill ◽  
R. Allen ◽  
P. McGeorge
Keyword(s):  
Object Tracking ◽  
Cognitive Processes ◽  
Spatial Location ◽  
Multiple Object Tracking ◽  
Tracking Performance ◽  
Multiple Object

The Multiple-Object Tracking paradigm has most commonly been utilized to investigate how subsets of targets can be tracked from among a set of identical objects. Recently, this research has been extended to examine the function of featural information when tracking is of objects that can be individuated. We report on a study whose findings suggest that, while participants can only hold featural information for roughly two targets this task does not affect tracking performance detrimentally and points to a discontinuity between the cognitive processes that subserve spatial location and featural information.

Location and identity are entangled in multiple-object tracking

2010 ◽  
Author(s):  
Todd S. Horowitz ◽  
Michael A. Cohen ◽  
Yair Pinto ◽  
Piers D. L. Howe
Keyword(s):  
Object Tracking ◽  
Multiple Object Tracking ◽  
Multiple Object
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