Spatial-Semantic and Temporal Attention Mechanism-Based Online Multi-Object Tracking

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

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.

A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

This paper provides a literature review of some of the most important concepts, techniques, and methodologies used within autonomous car systems. Specifically, we focus on two aspects extensively explored in the related literature: tracking, i.e., identifying pedestrians, cars or obstacles from images, observations or sensor data, and prediction, i.e., anticipating the future trajectories and motion of other vehicles in order to facilitate navigating through various traffic conditions. Approaches based on deep neural networks and others, especially stochastic techniques, are reported.

Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images

In recent years, increasing image data comes from various sensors, and object detection plays a vital role in image understanding. For object detection in complex scenes, more detailed information in the image should be obtained to improve the accuracy of detection task. In this paper, we propose an object detection algorithm by jointing semantic segmentation (SSOD) for images. First, we construct a feature extraction network that integrates the hourglass structure network with the attention mechanism layer to extract and fuse multi-scale features to generate high-level features with rich semantic information. Second, the semantic segmentation task is used as an auxiliary task to allow the algorithm to perform multi-task learning. Finally, multi-scale features are used to predict the location and category of the object. The experimental results show that our algorithm substantially enhances object detection performance and consistently outperforms other three comparison algorithms, and the detection speed can reach real-time, which can be used for real-time detection.

Ghost Detection and Removal Based on Two-Layer Background Model and Histogram Similarity

Detecting and removing ghosts is an important challenge for moving object detection because ghosts will remain forever once formed, leading to the overall detection performance degradation. To deal with this issue, we first classified the ghosts into two categories according to the way they were formed. Then, the sample-based two-layer background model and histogram similarity of ghost areas were proposed to detect and remove the two types of ghosts, respectively. Furthermore, three important parameters in the two-layer model, i.e., the distance threshold, similarity threshold of local binary similarity pattern (LBSP), and time sub-sampling factor, were automatically determined by the spatial-temporal information of each pixel for adapting to the scene change rapidly. The experimental results on the CDnet 2014 dataset demonstrated that our proposed algorithm not only effectively eliminated ghost areas, but was also superior to the state-of-the-art approaches in terms of the overall performance.