Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles. Since the annotations of point cloud data is an expensive and time-consuming process, therefore recently the utilisation of simulated environments and 3D LiDAR sensors for this task started to get some popularity. With simulated sensors and environments, the process for obtaining an annotated synthetic point cloud data became much easier. However, the generated synthetic point cloud data are still missing the artefacts usually exist in point cloud data from real 3D LiDAR sensors. As a result, the performance of the trained models on this data for perception tasks when tested on real point cloud data is degraded due to the domain shift between simulated and real environments. Thus, in this work, we are proposing a domain adaptation framework for bridging this gap between synthetic and real point cloud data. Our proposed framework is based on the deep cycle-consistent generative adversarial networks (CycleGAN) architecture. We have evaluated the performance of our proposed framework on the task of vehicle detection from a bird's eye view (BEV) point cloud images coming from real 3D LiDAR sensors. The framework has shown competitive results with an improvement of more than 7 in average precision score over other baseline approaches when tested on real BEV point cloud images.
On the other hand, in @cite_17 proposed deep-learning based approach for thermal infra-red object tracking. To overcome the scarcity of thermal images dataset, they utilised DA based on the CycleGAN architecture to transform images from visual domain to the thermal infra-red domain.
@cite_17
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Automated harvesting requires accurate detection and recognition of the fruit within a tree canopy in real-time in uncontrolled environments. However, occlusion, variable illumination, variable appearance and texture make this task a complex challenge. Our research discusses the development of a machine vision system, capable of recognizing occluded green apples within a tree canopy. This involves the detection of “green” apples within scenes of “green leaves”, shadow patterns, branches and other objects found in natural tree canopies. The system uses both thermal infra-red and color image modalities in order to achieve improved performance. Maximization of mutual information is used to find the optimal registration parameters between images from the two modalities. We use two approaches for apple detection based on low and high-level visual features. High-level features are global attributes captured by image processing operations, while low-level features are strong responses to primitive parts-based filters (such as Haar wavelets). These features are then applied separately to color and thermal infra-red images to detect apples from the background. These two approaches are compared and it is shown that the low-level feature-based approach is superior (74 recognition accuracy) over the high-level visual feature approach (53.16 recognition accuracy). Finally, a voting scheme is used to improve the detection results, which drops the false alarms with little effect on the recognition rate. The resulting classifiers acting independently can partially recognize the on-tree apples, however, when combined the recognition accuracy is increased. This chapter discusses the challenges of automating surveillance and reconnaissance tasks for infra-red visual data obtained from aerial platforms. These problems have gained significant importance over the years, especially with the advent of lightweight and reliable imaging devices. Detection and tracking of objects of interest has traditionally been an area of interest in the computer vision literature. These tasks are rendered especially challenging in aerial sequences of infra red modality. The chapter gives an overview of these problems, and the associated limitations of some of the conventional techniques typically employed for these applications. We begin with a study of various image registration techniques that are required to eliminate motion induced by the motion of the aerial sensor. Next, we present a technique for detecting moving objects from the ego-motion compensated input sequence. Finally, we describe a methodology for tracking already detected objects using their motion history. We substantiate our claims with results on a wide range of aerial video sequences. In this work, we propose a new integrated framework that addresses the problems of thermal-visible video registration, sensor fusion, and people tracking for far-range videos. The video registration is based on a RANSAC trajectory-to-trajectory matching, which estimates an affine transformation matrix that maximizes the overlapping of thermal and visible foreground pixels. Sensor fusion uses the aligned images to compute sum-rule silhouettes, and then constructs thermal-visible object models. Finally, multiple object tracking uses blobs constructed in sensor fusion to output the trajectories. Results demonstrate the advantage of our proposed framework in obtaining better results for both image registration and tracking than separate image registration and tracking methods. This paper investigates how to integrate the complementary information from RGB and thermal (RGB-T) sources for object tracking. We propose a novel Convolutional Neural Network (ConvNet) architecture, including a two-stream ConvNet and a FusionNet, to achieve adaptive fusion of different source data for robust RGB-T tracking. Both RGB and thermal streams extract generic semantic information of the target object. In particular, the thermal stream is pre-trained on the ImageNet dataset to encode rich semantic information, and then fine-tuned using thermal images to capture the specific properties of thermal information. For adaptive fusion of different modalities while avoiding redundant noises, the FusionNet is employed to select most discriminative feature maps from the outputs of the two-stream ConvNet, and updated online to adapt to appearance variations of the target object. Finally, the object locations are efficiently predicted by applying the multi-channel correlation filter on the fused feature maps. Extensive experiments on the recently public benchmark GTOT verify the effectiveness of the proposed approach against other state-of-the-art RGB-T trackers.