Small drones are a rising threat due to their possible misuse for illegal activities, in particular smuggling and terrorism. The project SafeShore, funded by the European Commission under the Horizon 2020 program, has launched the "drone-vs-bird detection challenge" to address one of the many technical issues arising in this context. The goal is to detect a drone appearing at some point in a video where birds may be also present: the algorithm should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds. This paper reports on the challenge proposal, evaluation, and results 1 .
The usage of small drones/UAVs has significantly increased recently. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. The similarity in the appearance of small drone and birds in complex background makes it challenging to detect drones in surveillance videos. This paper addresses the challenge of detecting small drones in surveillance videos using popular and advanced deep learning-based object detection methods. Different CNNbased architectures such as ResNet-101 and Inception with Faster-RCNN, as well as Single Shot Detector (SSD) model was used for experiments. Due to sparse data available for experiments, pre-trained models were used while training the CNNs using transfer learning. Best results were obtained from experiments using Faster-RCNN with the base architecture of ResNet-101. Experimental analysis on different CNN architectures is presented in the paper, along with the visual analysis of the test dataset.
As a rapid increase in urbanization, many people can afford a car nowadays but due to increase in the number of cars in the city people are facing problems in travelling. Due to rapid increase in the number of cars not only congestion and traffic is increasing but also it is harming the environment. Also due to this it leads to problems like increase in fuel combustion, heavy cost on resources, parking problems. To overcome these problems an online solution of "CAR POOLING" has been proposed. We intend to make a web and android application that will let people know if vehicles are available for sharing in their desired path. Also ,it will facilitate people using this application to share expense and may not worry about hiring a cab or making new connections. People using this application on their mobile phone can easily carpool with unacquainted or acquainted people without much efforts and even without security concerns. Intelligent transportation technology can play an important role in making these systems user-friendly, easy to manage, and efficient. There is an important need to tightly integrate the different types of technology and to develop an effective system architecture.
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