Search

To facilitate object localization, the existing convolutional neural network (CNN)-based object detection often requires an object proposal method, which, however, may produce inaccurate region proposals and thus impact the performance. To overcome this setback, this paper presents a novel iterative localization refinement method which, undertaken at a mid-layer of a CNN architecture, progressively refines a subset of region proposals in order to match as much ground-truth as possible. In each iteration, the refinement task is cast into a probabilistic framework based on an ingeniously devised probability function. To expedite the computation of the probability function, a divide-and-conquer paradigm is developed by the theorem of total probability. Moreover, an approximate variant based on a refined sampling strategy is also addressed to further reduce the complexity. The proposed ILR method is not only data-driven and free of learning, but it can also be incorporated with many existing CNN-based object detection algorithms, such as Faster R-CNN to enhance the detection accuracy without changing their configurations. Simulations show that the proposed method can improve the main state-of-the-art works on the PASCAL VOC 2007, 2012 and Youtube-Objects data sets. [ABSTRACT FROM AUTHOR]

Compared with other applications in computer vision, convolutional neural networks (CNNs) have underperformed on pedestrian detection. A breakthrough was made very recently using sophisticated deep CNN (DCNN) models, with a number of handcrafted features or explicit occlusion handling mechanism. In this paper, we show that by reusing the convolutional feature maps of a DCNN model as image features to train an ensemble of boosted decision models, we are able to achieve the best reported accuracy without using specially designed learning algorithms. We empirically identify and disclose important implementation details. We also show that pixel labeling may be simply combined with a detector to boost the detection performance. By adding complementary handcrafted features such as optical flow, the DCNN-based detector can be further improved. We advance the state-of-the-art results by lowering the log-average miss rate from 11.7% to 8.9% on the Caltech data set and from 11.2% to 8.6% on the Inria data set. We also achieve a comparable result to state-of-the-art approaches on the KITTI data set. [ABSTRACT FROM AUTHOR]