Detection of small moving objects is an important research area with applications including monitoring of flying insects, studying their foraging behavior, using insect pollinators to monitor flowering and pollination of crops, surveillance of honeybee colonies, and tracking movement of honeybees. However, due to the lack of distinctive shape and textural details on small objects, direct application of modern object detection methods based on convolutional neural networks (CNNs) shows considerably lower performance. In this paper we propose a method for the detection of small moving objects in videos recorded using unmanned aerial vehicles equipped with standard video cameras. The main steps of the proposed method are video stabilization, background estimation and subtraction, frame segmentation using a CNN, and thresholding the segmented frame. However, for training a CNN it is required that a large labeled dataset is available. Manual labelling of small moving objects in videos is very difficult and time consuming, and such labeled datasets do not exist at the moment. To circumvent this problem, we propose training a CNN using synthetic videos generated by adding small blob-like objects to video sequences with real-world backgrounds. The experimental results on detection of flying honeybees show that by using a combination of classical computer vision techniques and CNNs, as well as synthetic training sets, the proposed approach overcomes the problems associated with direct application of CNNs to the given problem and achieves an average F1-score of 0.86 in tests on real-world videos.

Traffic congestion occurs when traffic demand is greater than the available network capacity. It is characterized by lower vehicle speeds, increased travel times, arrival unreliability, and longer vehicular queueing. Congestion can also impose a negative impact on the society by decreasing the quality of life with increased pollution, especially in urban areas. To mitigate the congestion problem, traffic engineers and scientists need quality, comprehensive, and accurate data to estimate the state of traffic flow. Various types of data collection technologies have different advantages and disadvantages as well as data characteristics, such as accuracy, sampling frequency, and geospatial coverage. Multisource data fusion increases the accuracy and provides a comprehensive estimation of the performance of traffic flow on a road network. This paper presents a literature overview related to the estimation of congestion and prediction based on the data collected from multiple sources. An overview of data fusion methods and congestion indicators used in the literature for traffic state and congestion estimation is given. Results of these methods are analyzed, and a disseminative analysis of the advantages and disadvantages of surveyed methods is presented.