Analysis and visualization of data obtained from camera mounted on unmanned aerial vehicle used in areas of urban transport

Urban Traffic Monitoring and Analysis Using Unmanned Aerial Vehicles (UAVs): A Systematic Literature Review

“…In most cases, traffic analysis studies include elements of traffic monitoring: vehicles are first detected, tracked, then further analyses are performed. The analysis, visualization, and interpretation of data obtained from UAV cameras requires intelligent processing systems [77]. Thus, for the trajectory extraction of multiple vehicles, several steps are required: preprocessing, stabilization, georegistration, vehicle detection and tracking, and trajectory management [74].…”

“…For vehicle detection, some of the works used conventional computer vision techniques that are focused on feature extraction, such as interest point detection (Shi-Tomasi features) [73], scale invariant feature transform (SIFT) [67,70], histogram of oriented gradients (HOG) features [58,76], local binary patterns (LBP) [49], Viola-Jones object detection scheme [58,76], Haar-like features [56] together with classifiers like support vector machine (SVM) [81], AdaBoost classifier [49,58,76], or k-means clustering [70]. Moreover, for fully automatic techniques of tracking, traditional motion-based methods can be identified, e.g., optical flow (e.g., Kanade-Lucas algorithm) [73][74][75]84,86], background subtraction [61,74,75,77,80], particle filter [28,61,83], correlation filter [68], Kalman filter [65,75,78,82,87,92].…”

“…Regarding the variables that were taken into account for the evaluation of the proposed system, some authors used parameters such as speed [60,64,78,85], traffic density [66,73], vehicle counting [77,92], vehicle trajectory [80,91], and parameters related to the performance of developed method: precision [79], accuracy [81], F1 score [87], correctness, completeness, and quality [84,90]. In the vast majority of studies, there is no difference between the types of vehicles identified, but in some of the them, vehicles are classified in various categories, like cars, buses, trucks, motorbikes, and even pedestrians are detected in several studies.…”

Drone-Edge Coalesce for Energy-Aware and Sustainable Service Delivery for Smart City Applications

“…This clearly indicate that the collected information (i.e., images) are important and are further analysed to measure the surface temperature. Likewise, in [34], an the intelligent processing systems was introduced for the cameras mounted on the drones. The primary task of this system is the data acquisition and analysis, which helps to improve and predicts the urban transportation systems and its sustainability.…”

“…16 must be satisfied. If the criteria 3 is satisfied, then CC are added to the route matrix (R[]), otherwise the CC are removed from R[] (line [27][28][29][30][31][32][33][34][35]. After this, the data packet is transmitted over the selected congestion and collision-free and energy-efficient route (line 40).…”