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.

Automated crowd behaviour analysis and monitoring is a challenging task due to the unpredictable nature of the crowd within a particular scene and across different scenes. The prior knowledge of the type of scene under consideration is a crucial mid-level information, which could be utilized to develop robust crowd behaviour analysis systems. In this paper, we propose an approach to automatically detect the type of a crowded scene based on the global motion patterns of the objects within the scene. Three different types of scenes whose global motion pattern characteristics vary from uniform to non-uniform are considered in this work, namely structured, semi-structured, and unstructured scenes, respectively. To capture the global motion pattern characteristics of an input crowd scene, we first extract the motion information in the form of trajectories using a key-point tracker and then compute the average angular orientation feature of each trajectory. This paper utilizes these angular features to introduce a novel feature vector, termed as Histogram of Angular Deviations (HAD), which depicts the distribution of the pair-wise angular deviation values for each trajectory vector. Since angular deviation information is resistant to changes in scene perspectives, we consider it as a key feature for distinguishing the scene types. To evaluate the effectiveness of the proposed HAD-based feature vector in classifying the crowded scenes, we build a crowd scene classification model by training the classical machine learning algorithms on the publicly available Collective Motion Database. The experimental results demonstrate the superior crowd classification performance of the proposed approach as compared to the existing methods. In addition to this, we propose a technique based on quantizing the angular deviation values to reduce the feature dimension and subsequently introduce a novel crowd scene structuredness index to quantify the structuredness of an input crowded scene based on its HAD.

Motion pattern segmentation for crowded video scenes is an open problem because of the inability of existing approaches to tackle unpredictable crowd behaviour across varied scenes. To address this problem, we propose a Spatio-Angular Density-based Clustering (SADC) approach, which performs motion pattern segmentation by clustering the spatial and angular information obtained from the input trajectories. The knearest neighbours of each trajectory and the angular deviation between trajectories constitute the spatial and angular information, respectively. Effective integration of the spatio-angular information with an improvised density-based clustering algorithm makes this approach scene-independent. The performance of most clustering algorithms in the literature is parameter-driven. Choosing a single parameter value for different types of scenes decreases the overall clustering performance. In this paper, we have shown that our approach is robust to scene changes using a single threshold, and, through the analysis of parameters across eight commonly occurring crowded scenarios, we point out the range of thresholds that are suitable for each scene category. We evaluate the proposed approach on the benchmarked CUHK dataset. The experimental results show the superior clustering performance and execution speed of the proposed approach when compared to the state-of-the-art over different scene categories.

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The transformation of normal cervix to cervicitis as well as to cervical cancer is accompanied with biochemical alterations at cellular level. Laser induced fluorescence can reflect those changes either as variations in the fluorescence intensity or as shift in the fluorescence maxima of bio fluorophores present in tissues. The curve resolved fluorescence investigation of tissues under 325 nm excitation provides Collagen, bound NADH and free NADH as the discrimination factors between normal, cervicitis and cervical cancer. Even though the fluorescence emission intensity derived from collagen fiber is comparable in both normal and cervicitis, a considerable reduction was observed for the cervical cancer tissues compared to the former. Fluorescence corresponding to bound NADH is found to be reduced during the progression from normal to cervicitis and to cervical cancer, whereas the free NADH shows an opposite trend. The principal component analysis (PCA) was performed to obtain classification of spectral data from different categories on a reduced dimensional space. Furthermore, to test the usefulness of the recorded fluorescence spectra in discriminating the malignant and non-malignant (cervicitis and normal) samples, a supervised machine learning model based on Support Vector Machine (SVM) was built using the PCA-reduced data. The proposed SVM model was able to detect the malignant samples with a Sensitivity of 94.19% and Specificity of 96.51%. Moreover, the Raman spectral data from the corresponding tissue sites corroborate well with the observations derived from the fluorescence measurement. The results obtained in the present pilot study strongly suggests the potential of laser induced fluorescence technique combined with multivariate data analysis tool for the diagnosis of cervicitis and cervical malignancy.