TY - GEN
T1 - Interactively test driving an object detector
T2 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
AU - Anirudh, Rushil
AU - Turaga, Pavan
PY - 2014
Y1 - 2014
N2 - In this paper, we study the problem of 'test-driving' a detector, i.e. allowing a human user to get a quick sense of how well the detector generalizes to their specific requirement. To this end, we present the first system that estimates detector performance interactively without extensive ground truthing using a human in the loop. We approach this as a problem of estimating proportions and show that it is possible to make accurate inferences on the proportion of classes or groups within a large data collection by observing only 5 - 10% of samples from the data. In estimating the false detections (for precision), the samples are chosen carefully such that the overall characteristics of the data collection are preserved. Next, inspired by its use in estimating disease propagation we apply pooled testing approaches to estimate missed detections (for recall) from the dataset. The estimates thus obtained are close to the ones obtained using ground truth, thus reducing the need for extensive labeling which is expensive and time consuming.
AB - In this paper, we study the problem of 'test-driving' a detector, i.e. allowing a human user to get a quick sense of how well the detector generalizes to their specific requirement. To this end, we present the first system that estimates detector performance interactively without extensive ground truthing using a human in the loop. We approach this as a problem of estimating proportions and show that it is possible to make accurate inferences on the proportion of classes or groups within a large data collection by observing only 5 - 10% of samples from the data. In estimating the false detections (for precision), the samples are chosen carefully such that the overall characteristics of the data collection are preserved. Next, inspired by its use in estimating disease propagation we apply pooled testing approaches to estimate missed detections (for recall) from the dataset. The estimates thus obtained are close to the ones obtained using ground truth, thus reducing the need for extensive labeling which is expensive and time consuming.
UR - http://www.scopus.com/inward/record.url?scp=84904609464&partnerID=8YFLogxK
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U2 - 10.1109/WACV.2014.6836104
DO - 10.1109/WACV.2014.6836104
M3 - Conference contribution
AN - SCOPUS:84904609464
SN - 9781479949854
T3 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
SP - 175
EP - 182
BT - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
PB - IEEE Computer Society
Y2 - 24 March 2014 through 26 March 2014
ER -