The automatic detection of humans in aerial thermal imagery plays a significant role in various real-time applications, such as surveillance, search and rescue and border monitoring. Small target size, low resolution, occlusion, pose, and scale variations are the significant challenges in aerial thermal images that cause poor performance for various state-of-the-art object detection algorithms. Though many deep-learning-based object detection algorithms have shown impressive performance for generic object detection tasks, their ability to detect smaller objects in the aerial thermal images is analyzed through this study. This work carried out the performance evaluation of Faster R-CNN and single-shot multi-box detector (SSD) algorithms with different backbone networks to detect human targets in aerial view thermal images. For this purpose, two standard aerial thermal datasets having human objects of varying scale are considered with different backbone networks, such as ResNet50, Inception-v2, and MobileNet-v1. The evaluation results demonstrate that the Faster R-CNN model trained with the ResNet50 network architecture out-performed in terms of detection accuracy, with a mean average precision (mAP at 0.5 IoU) of 100% and 55.7% for the test data of the OSU thermal dataset and AAU PD T datasets, respectively. SSD with MobileNet-v1 achieved the highest detection speed of 44 frames per second (FPS) on the NVIDIA GeForce GTX 1080 GPU. Fine-tuning the anchor parameters of the Faster R-CNN ResNet50 and SSD Inception-v2 algorithms caused remarkable improvement in mAP by 10% and 3.5%, respectively, for the challenging AAU PD T dataset. The experimental results demonstrated the application of Faster R-CNN and SSD algorithms for human detection in aerial view thermal images, and the impact of varying backbone network and anchor parameters on the performance improvement of these algorithms.

Source camera identification process plays a major role in digital image forensics, which identifies the source camera used for capturing the given image. The state-of-the-art methods for source camera identification are mainly based on identifying the demosaicing artifacts, sensor noise based artifacts or by using image features from spatial/frequency domain. This paper proposes a novel feature based approach for source camera identification, by making use of the noise patterns present in the images, caused due to the individual source cameras. The scene contents present in the images are eliminated using Gaussian based denoising to estimate the noise patterns present in the images. Higher Order Wavelet Statistics (HOWS) are used as discriminative features and are used with support vector machine (SVM) classifiers to identify the originating source camera of the given image. The experiment has been carried out using the images captured from different cell phone cameras and the obtained results have proved the robustness of the proposed method.