MAOD: An Efficient Anchor-Free Object Detector Based on MobileDet

An Object Detector Trained on Line Drawings

We investigate line drawings as an alternative method to train an object detector. While traditional approaches rely on learning an object class from example images, we make use of abstraction provided by the artist. Our object class of choice are cats, posing a challenging detection problem by a variety of poses and appearances. In order to deal with this flexibility, and similar to part-based...

An efficient binary corner detector

Corner extraction is an important task in many computer vision systems. The quality of the corners and the efficiency of the detection method are two very important aspects that can greatly impact the accuracy, robustness and real-time performance of the corresponding corner-based vision system. In this paper we introduce a fast corner detector based on local binary-image regions. We verify the...

On rendering synthetic images for training an object detector

We propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of the target object. These parameters can then be reused to generate an unlimited line of training images of the object of interest in arbitra...

Generative part-based Gabor object detector

Discriminative part-based models have become the approach for visual object detection. The models learn from a large number of positive and negative examples with annotated class labels and location (bounding box). In contrast, we propose a part-based generative model that learns from a small number of positive examples. This is achieved by utilizing “privileged information”, sparse class-speci...

On Rendering Synthetic Images for Training an Object Detector