Evidential combination of pedestrian detectors

International audienceThe importance of pedestrian detection in many applications has led to the development of many algorithms. In this paper, we address the problem of combining the outputs of several detectors. A pre-trained pedestrian detector is seen as a black box returning a set of bounding boxes with associated scores. A calibration step is first conducted to transform those scores into a probability measure. The bounding boxes are then grouped into clusters and their scores are combined. Different combination strategies using the theory of belief functions are proposed and compared to probabilistic ones. A combination rule based on triangular norms is used to deal with dependencies among detectors. More than 30 state-of-the-art detectors were combined and tested on the Caltech Pedestrian Detection Benchmark. The best combination strategy outperforms the currently best performing detector by 9% in terms of log-average miss rate

Evidential Bagging: Combining Heterogeneous Classifiers in the Belief Functions Framework

International audienceIn machine learning, Ensemble Learning methodologies are known to improve predictive accuracy and robustness. They consist in the learning of many classifiers that produce outputs which are finally combined according to different techniques. Bagging, or Bootstrap Aggre-gating, is one of the most famous Ensemble methodologies and is usually applied to the same classification base algorithm, i.e. the same type of classifier is learnt multiple times on bootstrapped versions of the initial learning dataset. In this paper, we propose a bagging methodology that involves different types of classifier. Classifiers' probabilist outputs are used to build mass functions which are further combined within the belief functions framework. Three different ways of building mass functions are proposed; preliminary experiments on benchmark datasets showing the relevancy of the approach are presented

It takes two to tango: cascading off-the-shelf face detectors

Recent face detection methods have achieved high detection rates in unconstrained environments. However, as they still generate excessive false positives, any method for reducing false positives is highly desirable. This work aims to massively reduce false positives of existing face detection methods whilst maintaining the true detection rate. In addition, the proposed method also aims to sidestep the detector retraining task which generally requires enormous effort. To this end, we propose a two-stage framework which cascades two off-the-shelf face detectors. Not all face detectors can be cascaded and achieve good performance. Thus, we study three properties that allow us to determine the best pair of detectors. These three properties are: (1) correlation of true positives; (2) diversity of false positives and (3) detector runtime. Experimental results on recent large benchmark datasets such as FDDB and WIDER FACE support our findings that the false positives of a face detector could be potentially reduced by 90% whilst still maintaining high true positive detection rate. In addition, with a slight decrease in true positives, we found a pair of face detector that achieves significantly lower false positives, while being five times faster than the current state-of-the-art detector