Motivated by the center-surround mechanism in the human visual attention system, we propose to use average contrast maps for the challenge of pedestrian detection in street scenes due to the observation that pedestrians indeed exhibit discriminative contrast texture. Our main contributions are the first to design a local statistical multichannel descriptor to incorporate both color and gradient information. Second, we introduce a multidirection and multiscale contrast scheme based on grid cells to integrate expressive local variations. Contributing to the issue of selecting most discriminative features for assessing and classification, we perform extensive comparisons with respect to statistical descriptors, contrast measurements, and scale structures. By this way, we obtain reasonable results under various configurations. Empirical findings from applying our optimized detector on the INRIA and Caltech pedestrian datasets show that our features yield state-of-the-art performance in pedestrian detection.
Most computational approaches to visual attention determine center-surround contrasts by DoG-filters or approximations of these @cite_23 . Recently, several researchers represented the central and surrounding areas in terms of feature distributions so as to capture more information about the areas. These distributions were either discrete, in form of histograms @cite_48 , or continuous, fitted to a normal distribution @cite_49 , and various distance measures can be applied between central and surrounding distributions to quantify local contrast. However, we notice that the above strategies only achieve reasonable results for rather conspicuous scenarios, a big red flower standing out in surrounding green leaves. In fact, the background in our case is much more complex and the previous contrast models are not guaranteed to perform well. Consequently, we train and evaluate specialized contrast schemes in this paper and aim to find out the optimal configuration for our applications.
@cite_48 @cite_49 @cite_23
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In this paper, we introduce a new method to detect salient objects in images. The approach is based on the standard structure of cognitive visual attention models, but realizes the computation of saliency in each feature dimension in an information-theoretic way. The method allows a consistent computation of all feature channels and a well-founded fusion of these channels to a saliency map. Our framework enables the computation of arbitrarily scaled features and local center-surround pairs in an efficient manner. We show that our approach outperforms eight state-of-the-art saliency detectors in terms of precision and recall. Saliency is an attribute that is not included in an object itself, but arises from complex relations to the scene. Common belief in neuroscience is that objects are eye-catching if they exhibit an anomaly in some basic feature of human perception. This enables detection of object-like structures without prior knowledge. In this paper, we introduce an approach that models these object-to-scene relations based on probability theory. We rely on the conventional structure of cognitive visual attention systems, measuring saliency by local center to surround differences on several basic feature cues and multiple scales, but innovate how to model appearance and to quantify differences. Therefore, we propose an efficient procedure to compute ML-estimates for (multivariate) normal distributions of local feature statistics. Reducing feature statistics to Gaussians facilitates a closed-form solution for the W 2-distance (Wasserstein metric based on the Euclidean norm) between a center and a surround distribution. On a widely used benchmark for salient object detection, our approach, named CoDi-Saliency (for Continuous Distributions), outperformed nine state-of-the-art saliency detectors in terms of precision and recall. A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail.