We optimize a visual object detection application (that uses Vision Video Library kernels) and show that OpenCL is a unified programming paradigm that can provide high performance when running on the Ivy Bridge heterogeneous on-chip architecture. We evaluate different mapping techniques and show that running each kernel where it fits the best and using software pipelining can provide 1.91 times higher performance and 42% better energy efficiency. We also show how to trade accuracy for energy at runtime. Overall, our application can perform accurate object detection at 40 frames per second (fps) in an energy-efficient manner.

Most image understanding algorithms begin with the extraction of information thought to be relevant to the particular task. This is commonly known as feature extraction and has, up to this date, been a largely manual process, where a reasonable method is chosen through validation on the experimented dataset. In this work we propose a data driven, local histogram based feature extraction method that reduces the manual intervention during the feature computation process and improves on the performance of widely used gradient histogram based features (e.g., HOG). We demonstrate favorable object detection results against HOG on the Inria Pedestrian[7], Pascal 2007[10] data.

Classifying images using features extracted from densely sampled local patches has enjoyed significant success in many detection and recognition tasks. It is also well known that generally more than one type of feature is needed to achieve robust classification performance. Previous works using multiple features have addressed this issue either through simple concatenation of feature vectors or through combining feature specific kernels at the classifier level. In this work we introduce a novel approach for combining features at the feature level by projecting two types of features onto two respective subspaces in which they are maximally correlated. We use their correlation as an augmented feature and demonstrate improvement in classification accuracy over simple combination through concatenation in a pedestrian detection framework.

We investigate effective means of building robust dictionaries for detecting the sparse foreground in videos with static background. This work is an extension to our existing solution to foreground/background segmentation problem using the linear programming method proposed to detect sparse errors in signals, which are created by a known dictionary. The dictionary building methods we study are established robust component analysis techniques in the literature (i.e. k-SVD & robust-PCA) as well as a heuristic (running median) inspired by the highly correlated nature of the static video background signal. We compare the effectiveness of the new methods with our original system as well as a baseline method, which is the commonly used single Gaussian model of the background pixels.

Frames of videos with static background and dynamic foreground can be viewed as samples of signals that vary slowly in time with sparse corruption caused by foreground objects. We cast background subtraction as a signal estimation problem, where the error sparsity is enforced through minimization of the L1norm of the difference between the processed frame and estimated background subspace, as an approximation to the underlying L0norm minimization structure. Our work provides a novel framework for background subtraction with the added benefit of easy integration of local discriminative information (e.g. gradient, texture, motion field etc.) for improved robustness. We show that the proposed method is able to overcome various difficulties frequently encountered in real application settings, and is competitive with the state of the art.

This paper studies the problem of recognizing gender from full body images. This problem has not been addressed before, partly because of the variant nature of human bodies and clothing that can bring tough difficulties. However, gender recognition has high application potentials, e.g. security surveillance and customer statistics collection in restaurants, supermarkets, and even building entrances. In this paper, we build a system of recognizing gender from full body images, taken from frontal or back views. Our contributions are three-fold. First, to handle the variety of human body characteristics, we represent each image by a collection of patch features, which model different body parts and provide a set of clues for gender recognition. To combine the clues, we build an ensemble learning algorithm from those body parts to recognize gender from fixed view body images (frontal or back). Second, we relax the fixed view constraint and show the possibility to train a flexible classifier for mixed view images with the almost same accuracy as the fixed view case. At last, our approach is shown to be robust to small alignment errors, which is preferred in many applications.