A Robust Quadruplet and Faster Region-Based CNN for UAV Video-Based Multiple Object Tracking in Crowded Environment

Abstract

Multiple object tracking (MOT) from unmanned aerial vehicle (UAV) videos has faced several challenges such as motion capture and appearance, clustering, object variation, high altitudes, and abrupt motion. Consequently, the volume of objects captured by the UAV is usually quite small, and the target object appearance information is not always reliable. To solve these issues, a new technique is presented to track objects based on a deep learning technique that attains state-of-the-art performance on standard datasets, such as Stanford Drone and Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking (UAVDT) datasets. The proposed faster RCNN (region-based convolutional neural network) framework was enhanced by integrating a series of activities, including the proper calibration of key parameters, multi-scale training, hard negative mining, and feature collection to improve the region-based CNN baseline. Furthermore, a deep quadruplet network (DQN) was applied to track the movement of the captured objects from the crowded environment, and it was modelled to utilize new quadruplet loss function in order to study the feature space. A deep 6 Rectified linear units (ReLU) convolution was used in the faster RCNN to mine spatial–spectral features. The experimental results on the standard datasets demonstrated a high performance accuracy. Thus, the proposed method can be used to detect multiple objects and track their trajectories with a high accuracy.