A comparison of tracking algorithm performance for objects in wide area imagery

Philip, Rohit C.; Ram, Sundaresh; Gao, Xin; Rodríguez, Jeffrey J.

doi:10.1109/ssiai.2014.6806041

Cited by 11 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are in the literature some measures that allow the performance evaluation for tracking systems, but the ideal disparity test should be given by the comparison with a ground truth or the point to point comparison of each instant for all the tracks, as done by Philip et al (2014) Fang et al (2017), or Chau et al (2004. Unfortunately, the SCA module does not offer that information to compare the differences between each path with the one obtained by our proposed method.…”

Section: Resultsmentioning

confidence: 99%

Objective evaluation of ram and buck sperm motility by using a novel sperm tracker software

Zalazar

Pastore²,

Greco

et al. 2018

Reproduction

View full text Add to dashboard Cite

This work offers researchers the first version of an open-source sperm tracker software (Sperm Motility Tracker, V1.0) containing a novel suit of algorithms to analyze sperm motility using ram and buck sperm as models. The computer-assisted semen analysis is used in several publications with increasing trend worldwide in the last years, showing the importance of objective methodologies to evaluate semen quality. However, commercial systems are costly and versatility is constrained. In the proposed method, segmentation is applied and the tracking stage is performed by using individual Kalman filters and a simplified occlusion handling method. The tracking performance in terms of precision (number of true tracks), the percentage of fragmented paths and percentage of correctly detected particles were manually validated by three experts and compared with the performance of a commercial motility analyzer (Microptic's SCA). The precision obtained with our sperm motility tracker was higher than the one obtained with a commercial software at the current acquisition frame rate of 25 fps ( < 0.0001), concomitantly with a similar percentage of fragmentized tracks ( = 0.0709) at sperm concentrations ranging 25-37 × 10 cells/mL. Moreover, our tracker was able to detect trajectories that were unseen by SCA. Kinetic values obtained by using both methods were contrasted. The higher values found were explained based on the better performance of our sperm tracker to report speed parameters for very fast motile sperm. To standardize results, acquisition conditions are suggested. This open-source sperm tracker software has a good plasticity allowing researchers to upgrade according requirements and to apply the tool for sperm from a variety of species.

show abstract

Section: Resultsmentioning

confidence: 99%

Objective evaluation of ram and buck sperm motility by using a novel sperm tracker software

Zalazar

Pastore²,

Greco

et al. 2018

Reproduction

View full text Add to dashboard Cite

show abstract

“…Many studies have been conducted to track the target regions in nonthermal images. 25 Some of these tracking methods can also be used for thermal images. In this study, the forehead and the cheek skin temperatures were tracked automatically based on visible imaging techniques.…”

Section: Tracking the Regions Of Interestsmentioning

confidence: 99%

Driver drowsiness detection using facial thermal imaging in a driving simulator

Tashakori

Nahvi

Kiashari

2021

Proc Inst Mech Eng H

View full text Add to dashboard Cite

Driver drowsiness causes fatal driving accidents. Thermal imaging is a suitable drowsiness detection method as it is non-invasive and robust against changes in the ambient light. In this paper, driver drowsiness is detected by measuring the forehead temperature at the region covering the supratrochlear artery and also the cheek temperature. About 30 subjects drove on a highway in a driving simulator in two sessions. A thermal camera was used to monitor the facial temperature pattern. The subjects’ drowsiness levels were estimated by three human observers. The forehead and the cheek regions were located and tracked in each frame. The forehead and the cheek skin temperatures were obtained at three levels of drowsiness. The Support Vector Machine, the K-Nearest Neighbor, and the regression tree classifiers were used. From wakefulness to extreme drowsiness, the forehead skin temperature and the absolute cheek-forehead skin temperature gradient decreased by 0.46°C and 0.81°C, respectively. But the cheek skin temperature increased by 0.35°C in two sessions. The gradient difference is on average 50% higher than the forehead or the cheek temperature change alone. The results indicate that drowsiness can be detected with an accuracy of 82%, sensitivity of 85%, specificity of 90%, and precision of 84%. Driver drowsiness can be detected by monitoring changes in the forehead and the cheek temperature signal. Also, the temperature gradient can be used as a more robust and sensitive indicator of drowsiness.

show abstract

“…We also suggest improving the adapted MMA algorithm by adding a frame differencing method as a pre-processing step, then exploiting better multi-scale feature selection to improve average F-score with reference to some of the latest models [35]- [44] on object detection and their post-processing schemes [45]. Furthermore, we are investigating some of the recent deep learning schemes [46]- [57] for detecting and tracking vehicles [27], [34], [39], [58]- [61] in accordance with the complexity analysis [45], [62]- [69] from the deep CNN-based multi-object detection and segmentation schemes [48]- [51], [53]- [56], [59]- [61], [64]- [66], [70]- [72] applied to wide-area aerial surveillance.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Spatio-Temporal Processing for Automatic Vehicle Detection in Wide-Area Aerial Video

et al. 2020

Self Cite

View full text Add to dashboard Cite

Vehicle detection in aerial videos often requires post-processing to eliminate false detections. This paper presents a spatio-temporal processing scheme to improve automatic vehicle detection performance by replacing the thresholding step of existing detection algorithms with multi-neighborhood hysteresis thresholding for foreground pixel classification. The proposed scheme also performs spatial post-processing, which includes morphological opening and closing to shape and prune the detected objects, and temporal post-processing to further reduce false detections. We evaluate the performance of the proposed spatial processing on two local aerial video datasets and one parking vehicle dataset, and the performance of the proposed spatio-temporal processing scheme on five local aerial video datasets and one public dataset. Experimental evaluation shows that the proposed schemes improve vehicle detection performance for each of the nine algorithms when evaluated on seven datasets. Overall, the use of the proposed spatio-temporal processing scheme improves average F-score to above 0.8, achieves a 90% reduction in false positives and reduces percentage of wrong classifications to below 50% for some of the nine algorithms. INDEX TERMS Vehicle detection, hysteresis thresholding, spatio-temporal processing, wide-area aerial imagery.

show abstract

A comparison of tracking algorithm performance for objects in wide area imagery

Cited by 11 publications

References 14 publications

Objective evaluation of ram and buck sperm motility by using a novel sperm tracker software

Objective evaluation of ram and buck sperm motility by using a novel sperm tracker software

Driver drowsiness detection using facial thermal imaging in a driving simulator

Spatio-Temporal Processing for Automatic Vehicle Detection in Wide-Area Aerial Video

Contact Info

Product

Resources

About