<p>Traffic video analytics has become one of the core components in the evolution of transportation systems. Artificially intelligent traffic management systems apply computer vision techniques to alleviate the monotony of manually monitoring the video feed from surveillance cameras. Locating the objects of interest is the most crucial step in the pipeline of such video analytics systems. An abundance of research has been conducted to find the location of the targets in traffic scenes. This paper presents a comprehensive review of different algorithms used for object detection in traffic surveillance applications in addition to the recent trends and future directions. Based on the approaches used in the related studies, we categorize the object detection methods into motion-based and appearance-based techniques. We further classify each group of techniques into a number of subcategories and analyze the advantages and disadvantages of each method. The major challenges, limitations, and potential solutions are also discussed along with the future scope and directions.</p>

<p>Traffic video analytics has become one of the core components in the evolution of transportation systems. Artificially intelligent traffic management systems apply computer vision techniques to alleviate the monotony of manually monitoring the video feed from surveillance cameras. Locating the objects of interest is the most crucial step in the pipeline of such video analytics systems. An abundance of research has been conducted to find the location of the targets in traffic scenes. This paper presents a comprehensive review of different algorithms used for object detection in traffic surveillance applications in addition to the recent trends and future directions. Based on the approaches used in the related studies, we categorize the object detection methods into motion-based and appearance-based techniques. We further classify each group of techniques into a number of subcategories and analyze the advantages and disadvantages of each method. The major challenges, limitations, and potential solutions are also discussed along with the future scope and directions.</p>

Background：The mink exhibits embryo diapause after fertilization.Mink embryos enter a period of diapause after the embryo develops into the blastocyst. The specific process of embryo diapause regulation and reactivation is remains largely un-examined. The aim of this study was to identify key factors associated with mink embryo diapause and reactivation by comparing and analyzing differences in serum metabolites up to twenty-nine days after mating. Material and methods: Blood samples were taken on the first day of mating, and then once a week until the fifth week. Metabolomic profiles of the serum samples taken during this period were analysed by ultra-performance liquid chromatography/mass spectrometry. Results: Multivariate statistical analyses identified differential metabolite expression at different time points in both positive ion mode and negative ion modes. Dopamine may be the major inhibitory compound for mink embryo reactivation; moreover, and the levels of L-proline, L-threonine, taurine, D-ornithine, L-valine, L-kynurenine, and particularly L-leucine may be related to embryo reactivation. Conclusions: We compared blood serum metabolites at different stages in the pregnancy. The study makes a significant contribution to the literature as we found that dopamine is a strong candidate as an inhibitor of embryo reactivation.The study showed that levels of seven amino acids, but especially L-leucine, may be correlated with embryo reactivation.