Selecting Optimal Proposal Number for Image-based Object Detection

Abstract

In order to balance the detection time and accuracy, the state-of-the-art region-based detectors use a fixed number of proposals to obtain detection results in the inference phase. However, in surveillance scenes, object population varies in different images, causing the fixed proposal number becomes an undeterminable hyper-parameter, which needs to be correspondingly adjusted to maintain high recall. To solve this problem, we propose two image-level optimal proposal number selection methods called linear proposal number (LPN) selection method and adaptive proposal number (APN) selection method respectively, both aiming at selecting an optimal proposal number for each image to adapt both the images with sparsely and densely distributed objects. In LPN selection method, we introduce a linear weighting hyper-parameter to formulate the relationship between the actual object number and proposals’ scores to obtain the optimal proposal number. To avoid setting the hyper-parameter manually, we further propose another APN selection method where the optimal proposal number of each image is selected by exploring the distribution of the proposals’ scores. Results obtained from the UA-DETRAC car dataset and self-built bird dataset (BSBDV 2017) show that our proposed methods can largely improve the detection performance in terms of detection time and accuracy without any re-training process.