# A Hybrid Retina Net Classifier for Thermal Imaging
## 2 Literature Survey  
Thermal imaging finds an extensive application during nighttime, primarily proving its usefulness in securing national borders. Because of the lack of visible light, certain object features become challenging to track. As a solution, a CNN trained on visible images is adapted for thermal image tracking. An ensemble tracker based on correlation filter is proposed in [6], which has the capability of convolutional features extended for multiple layers rather than a single layer. Long wave infrared sensor (LWIR), as the name itself implies, could sense objects within the range of the invisible IR spectrum. Target detection and false alarm rates are improved using LWIR sensors in the work presented in [7]. The effectiveness of combining a CNN with background modeling for human detection is proposed and demonstrated by [PERSON] et al. [8]. Improved Gaussian average and human classification using CNN is only performed for foreground objects in real time. The technique to detect in real time humans among images, which are thermal and built by background modeling, CNN, is explained in [8]. Object detection using retina net gives prominently better results compared with the other existing methods. The temporal characteristics of an image add an advantage in extracting the features of a particular image more accurately [9]. When a sequence of images is used, rather than a single image, 21.4% improvement in performance is achieved. When a long-range image is to be detected, the number of pixels on the target image obviously becomes less as the distance increases. [PERSON] et al. presented a resolution method that was proposed as a solution to address this issue. The method not only increases the resolution of the image but also enhances the baseline quality of inputs for object recognition. The system was tested using two datasets that included pedestrians and six distinct types of vehicles [10]. A research study [11] that focuses on techniques and systems implemented on Raspberry Pi covers various aspects, including machine learning models, feature extraction techniques, and datasets. The recognition and detection of humans using thermal images, videos, and the utilization of different wavelengths are expanding, creating a demand for research in the field of machine vision, deep learning, and domains, such as infrared. Researchers in thermal imaging commonly show interest in detecting humans and exploring techniques that combine thermal imaging with images captured at different wavelengths [12]. The writers will estimate pixel-level images combining infrared and RGB images to refine the detectors of pedestrians built on CNN, which will work during the day or night and that are useful for video surveillance, autonomous vehicles, and advanced driver-assistance systems (ADAS). In [13], the authors reported a model based on a nine-layer CNN called self-learning soft max which utilizes the near-infrared images to identify the pedestrians. Many samples were collected for testing the CNN-based model and found that this model provides better results in terms of pedestrian recognition and accuracy. [PERSON] et al. [14] introduced a narrative descriptor of saliency awareness known as SSDI, which means stacked saliency difference image, to design spatial-temporal, local, and global movement data to human action recognition (HAR) among infrared images of infrared. Here they used a four-stream deep framework built on CNN and RNN, known as RNN designs, and obtained a result of 83.5% on the dataset of InfAR [15] and a baseline result of 75.17% with the suggested dataset IITR-IAR [14]. The use of CNN to detect humans, their recognition, and classification of action are discussed in [16; 17; 18; 19; 20]. The Caltech pedestrian dataset [21; 22] introduced as a benchmark for pedestrian detection, surpasses existing datasets in terms of scale and includes various videos, such as low-resolution images and videos captured from moving vehicles. The availability of this dataset opens new research possibilities.