Group Norm for Learning Structured SVMs with Unstructured Latent Variables

Latent variables models have been applied to a number of computer vision problems. However, the complexity of the latent space is typically left as a free design choice. A larger latent space results in a more expressive model, but such models are prone to over fitting and are slower to perform inference with. The goal of this paper is to regularize the complexity of the latent space and learn which hidden states are really relevant for prediction. Specifically, we propose using group-sparsity-inducing regularizers such as ℓ[subscript 1]-ℓ[subscript 2] to estimate the parameters of Structured SVMs with unstructured latent variables. Our experiments on digit recognition and object detection show that our approach is indeed able to control the complexity of latent space without any significant loss in accuracy of the learnt model.Quanta Computer (Firm)Google (Firm

Применение алгоритмов искусственного интеллекта для определения буллинга в видеопотоке в средних учебных заведениях

The article presents the basic concept of development of software for determination of bulling in the video stream.В статье представлена основная концепция разработки ПО для определения буллинга в видеопотоке

Алгоритмы определения и оценки позы человека на изображении и видеопоследовательности

The article presents the basic concept of developing algorithms for determining and evaluating the position of a person on an image and a video sequence.В статье представлена основная концепция разработки алгоритмов определения и оценки позы человека на изображении и видеопоследовательности

Learning Separable Filters

Learning filters to produce sparse image representations in terms of overcomplete dictionaries has emerged as a powerful way to create image features for many different purposes. Unfortunately, these filters are usually both nu-merous and non-separable, making their use computation-ally expensive. In this paper, we show that such filters can be computed as linear combinations of a smaller number of separable ones, thus greatly reducing the computational complexity at no cost in terms of performance. This makes filter learning approaches practical even for large images or 3D volumes, and we show that we significantly outperform state-of-the-art methods on the linear structure extraction task, in terms of both accuracy and speed. Moreover, our approach is gen-eral and can be used on generic filter banks to reduce the complexity of the convolutions. 1

Tracking Multiple Players using a Single Camera

It has been shown that multi-people tracking could be successfullly formulated as a Linear Program to process the output of multiple fixed and synchronized cameras with overlapping fields of view. In this paper, we extend this approach to the more challenging single-camera case and show that it yields excellent performance, even when the camera moves. We validate our approach on a number of basketball matches and argue that using a properly retrained people detector is key to producing the probabilities of presence that are used as input to the Linear Program

The Fastest Deformable Part Model for Object Detection

This paper solves the speed bottleneck of deformable part model (DPM), while maintaining the accuracy in de-tection on challenging datasets. Three prohibitive steps in cascade version of DPM are accelerated, including 2D cor-relation between root filter and feature map, cascade part pruning and HOG feature extraction. For 2D correlation, the root filter is constrained to be low rank, so that 2D cor-relation can be calculated by more efficient linear combi-nation of 1D correlations. A proximal gradient algorithm is adopted to progressively learn the low rank filter in a dis-criminative manner. For cascade part pruning, neighbor-hood aware cascade is proposed to capture the dependence in neighborhood regions for aggressive pruning. Instead of explicit computation of part scores, hypotheses can be pruned by scores of neighborhoods under the first order ap-proximation. For HOG feature extraction, look-up tables are constructed to replace expensive calculations of orien-tation partition and magnitude with simpler matrix index operations. Extensive experiments show that (a) the pro-posed method is 4 times faster than the current fastest DPM method with similar accuracy on Pascal VOC, (b) the pro-posed method achieves state-of-the-art accuracy on pedes-trian and face detection task with frame-rate speed. 1

Random Forest with Adaptive Local Template for Pedestrian Detection

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance