You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

The most successful 2D object detection methods require a large number of images
annotated with object bounding boxes to be collected for training. We present an alternative …

We consider the problem of designing a scene-specific pedestrian detector in a scenario
where we have zero instances of real pedestrian data (ie, no labeled real data or …

The accuracy of object classifiers can significantly drop when the training data (source
domain) and the application scenario (target domain) have inherent differences. Therefore …

Despite the fact that face detection has been studied intensively over the past several
decades, the problem is still not completely solved. Challenging conditions, such as extreme …

Instance transfer approaches consider source and target data together during the training
process, and borrow examples from the source domain to augment the training data, when …

We consider scenarios where we have zero instances of real pedestrian data (eg, a newly
installed surveillance system in a novel location in which no labeled real data or …

The use of local features for image representation has become popular in recent years.
Local features are often used in the bag-of-visual-words scheme. Although proven effective …

Recently, pedestrian detection has become an important problem of interest. Our work
primarily depends on robust and fast deep neural network architectures. This paper used …

In this paper we propose a fast head-shoulder detector as a means to facilitating faster
pedestrian detection. The proposed approach is based on the observation that human head …