Vision based player detection is important in sports applications. Accuracy, efficiency, and low memory consumption are desirable for real-time tasks such as intelligent broadcasting and automatic event classification. In this paper, we present a cascaded convolutional neural network (CNN) that satisfies all three of these requirements. Our method first trains a binary (player/non-player) classification network from labeled image patches. Then, our method efficiently applies the network to a whole image in testing. We conducted experiments on basketball and soccer games. Experimental results demonstrate that our method can accurately detect players under challenging conditions such as varying illumination, highly dynamic camera movements and motion blur. Comparing with conventional CNNs, our approach achieves state-of-the-art accuracy on both games with 1000× fewer parameters (i.e., it is light).
Road detection is an essential component of field robot navigation systems. Vision sensors play an important role in road detection for their great potential in environmental perception. In this paper, we propose a hierarchical vision sensor-based method for robust road detection in challenging road scenes. More specifically, for a given road image captured by an on-board vision sensor, we introduce a multiple population genetic algorithm (MPGA)-based approach for efficient road vanishing point detection. Superpixel-level seeds are then selected in an unsupervised way using a clustering strategy. Then, according to the GrowCut framework, the seeds proliferate and iteratively try to occupy their neighbors. After convergence, the initial road segment is obtained. Finally, in order to achieve a globally-consistent road segment, the initial road segment is refined using the conditional random field (CRF) framework, which integrates high-level information into road detection. We perform several experiments to evaluate the common performance, scale sensitivity and noise sensitivity of the proposed method. The experimental results demonstrate that the proposed method exhibits high robustness compared to the state of the art.
Road detection is a crucial problem for autonomous navigation system (ANS) and advance driverassistance system (ADAS). In this paper, we propose a hierarchical road detection method for robust road detection in challenging scenarios. Given an on-board road image, we first train a Gaussian mixture model (GMM) to obtain road probability density map (RPDM), and next oversegment the image into superpixels. Based on RPDM and superpixels, initial seeds are selected in an unsupervised way, and the seed superpixels iteratively try to occupy their neighbors according to GrowCut framework, the road segment is obtained after convergency. Finally, we refine the road segment with a conditional random field (CRF), which enforces the shape prior on the road segmentation task. Experiments on two challenging databases demonstrate that the proposed method exhibits high robustness compared with the state-of-the-art.
Cutaneous Malignant Melanoma (CMM) incidence has been rising around the world and over the last three decades at rates greater than for any other malignancy. Our objective was to describe geographic trends in incidence and mortality of CMM in Russia between 2001 and 2017 using geo-informatics technique (mapping) and descriptive statistical analysis. Additionally, we aimed to study the associations between ethnicity, geographic latitude/longitude and CMM incidence/mortality rates. We retrospectively analyzed the data from the Moscow Oncology Research Institute, Ministry of Health of Russian Federation for the period of the study. International Classification of Diseases (ICD) C43 code (comprising C43.0-C.43.9) was used to identify cutaneous melanoma cases. Routine methods of descriptive epidemiology were used to study incidence and mortality rates by age groups, years, and jurisdictions (i.e., Federal Districts and Federal Subjects of Russia). In total 141,597 patients were diagnosed with melanoma in Russia over the period 2001-2017, of which 62% were women (p<0.001). The overall age-standardized incidence and mortality rates were 4.27/100,000 and 1.62/ 100,000, respectively. Geographic mapping revealed North-to-South and East-to-West gradients across the country. Intrinsic patient characteristics such as the skin phenotype and the climate zones of the country could be an important risk factors for melanoma development. This study, for the first time, reports the burden and geographic distribution of CMM in Russia and the trends correlate with observations in countries with similar geography.
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