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IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Measures of effective video tracking
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Measures of Effective Video Tracking
Abstract—To evaluate multitarget video tracking results, one needs to quantify the ccuracy
of the estimated target-size and the cardinality error as well as measure the frequency of
occurrence of ID changes. In this paper, we survey existing multitarget tracking
performance scores and, afte r discussing their limitations, we propose three parameter-independent
measures for evaluating multitarget video tracking. The measures consider
target-size variations, combine accuracy and cardinality errors, quantify long-term
tracking accuracy at different accuracy levels, and evaluate ID changes relative to the
duration of the track in which they occur. We conduct an extensive experimental validation
of the proposed measures by comparing them with existing ones and by evaluating four
state-of-the-art trackers on challenging realworld publicly-available data sets. The software
implementing the proposed measures is made available online to facilitate their use by the
research community.
Existing method:
Discrepancy-based performance assessment operates at frame level or at sequence level by
considering either individual tracks. In previously we are applying singe frame or single
dimension person tracking. Complexity of noise level is very high.
Propose method:
vIDEO tracking is a widely researched topic with applications in event detection, surveillance
and behavior
analysis. These applications may involve simultaneous tracking of multiple moving targets using
a point-target representation (e.g. feature-point tracking) or an extended-target representation
(e.g. in face or person tracking) [1]–[6]. Pointtarget representations use target position
information, whereas extended-target representations also include information about the region
covered by the target in the image plane. We can identify three categories of multi-target
tracking evaluation measures: Point-based Assignment and Position-based (PAP) evaluation,
2. Region-based Assignment and Position-based (RAP) evaluation, and Region-based Assignment
and Size-based (RAS) evaluation. These three categories are discussed after the definition of the
notation we will use in this paper.
Merits:
1. Single frame tracking
2. Noise level is very high compared to state-of-art
Demerits:
1. To measure multi frame detection
2. To measure PAP,RAP,RAS.
RESULTS:
3.
4. Fig. Example of target occlusion in Bahnhof sequence. DP-NMS (green bounding box) loses the
target due to occlusion in (b); however, the other trackers successfully handle it. Yellow: MT-TBD;
cyan: CRFBT; magenta: ground truth.
![GLOBALSOFT TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401
Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com
Measures of Effective Video Tracking
Abstract—To evaluate multitarget video tracking results, one needs to quantify the ccuracy
of the estimated target-size and the cardinality error as well as measure the frequency of
occurrence of ID changes. In this paper, we survey existing multitarget tracking
performance scores and, afte r discussing their limitations, we propose three parameter-independent
measures for evaluating multitarget video tracking. The measures consider
target-size variations, combine accuracy and cardinality errors, quantify long-term
tracking accuracy at different accuracy levels, and evaluate ID changes relative to the
duration of the track in which they occur. We conduct an extensive experimental validation
of the proposed measures by comparing them with existing ones and by evaluating four
state-of-the-art trackers on challenging realworld publicly-available data sets. The software
implementing the proposed measures is made available online to facilitate their use by the
research community.
Existing method:
Discrepancy-based performance assessment operates at frame level or at sequence level by
considering either individual tracks. In previously we are applying singe frame or single
dimension person tracking. Complexity of noise level is very high.
Propose method:
vIDEO tracking is a widely researched topic with applications in event detection, surveillance
and behavior
analysis. These applications may involve simultaneous tracking of multiple moving targets using
a point-target representation (e.g. feature-point tracking) or an extended-target representation
(e.g. in face or person tracking) [1]–[6]. Pointtarget representations use target position
information, whereas extended-target representations also include information about the region
covered by the target in the image plane. We can identify three categories of multi-target
tracking evaluation measures: Point-based Assignment and Position-based (PAP) evaluation,](https://image.slidesharecdn.com/measuresofeffectivevideotracking-140919025223-phpapp02/85/IEEE-2014-MATLAB-IMAGE-PROCESSING-PROJECTS-Measures-of-effective-video-tracking-1-320.jpg){kind=tracking}
