Qualitative Evaluation of Detection and Tracking Performance

Swaminathan Sankaranarayanan (1) , Francois Bremond (1) , David Tax (2)

1 STARS - Spatio-Temporal Activity Recognition Systems
2 PR&B Lab

A new evaluation approach for detection and tracking systems is presented in this work. Given an algorithm that detects people and simultaneously tracks them, we evaluate its output by considering the complexity of the input scene. Some videos used for the evaluation are recorded using the Kinect sensor which provides for an automated ground truth acquisition system. To analyze the algorithm performance, a number of reasons due to which an algorithm might fail is investigated and quantified over the entire video sequence. A set of features called Scene Complexity measures are obtained for each input frame. The variability in the algorithm performance is modeled by these complexity measures using a polynomial regression model. From the regression statistics, we show that we can compare the performance of two different algorithms and also quantify the relative influence of the scene complexity measures on a given algorithm.

Domaines: Traitement des images [eess.IV]

Swaminathan Sankaranarayanan, Francois Bremond, David Tax. Qualitative Evaluation of Detection and Tracking Performance. 9th IEEE International Conference On Advanced Video and Signal Based Surveillance (AVSS 12), Sep 2012, Beijing, China. pp.362-367, ⟨10.1109/AVSS.2012.57⟩. ⟨hal-00763587⟩