## 2 Related Works and Motivation
The employed CNN network model had another layer added to it known as a position-sensitive region of interest (RoI) pooling layer, based on ZFnet, to further improve its accuracy. From their experiments, the proposed model was able to detect hands and three other classes accurately, achieving a mAP of 91.6%. On the other hand, [PERSON] et al. [17] implemented two popular CNN backbones, the VGG-16 and ResNet 50 to compare their performance in a hand detection system. Both backbones were trained on the same dataset, ImageNet, to ensure a fair comparison. Additional features such as rotational map were introduced to the CNN network to enable the detector to draw bounding boxes near rotated hands in images. Implementation results showed a significant improvement in mAP, with the VGG-16 network achieving 92.3% mAP and a frame rate of 13.10 FPS and ResNet 50 attaining 94.8% mAP and frame rate of 19.80 FPS, outperforming the other existing detection benchmarks such as YOLO (76.4% mAP and frame rate of 35 FPS). In [PERSON] et al. [18], the existing SSD network was improved by replacing the original backbone of SSD, VGG-16 net with ResNet 101, and fusing the feature maps' three Conv4 layers with two Conv6 layers. These improvements are aimed at improving the accuracy of the existing SSD network in detecting smaller objects, such as distanced hand gestures to a space robot in a space station. Results from their proposed design showed significant enhancement of SSD model in terms of mAP, as their model was able to achieve mAP of 89.4% on public hand datasets such as the Oxford hands dataset [19], EgoHands dataset [20], and self-developed Space Robot Simple Sign Language (SRSSL) dataset [18]. In another recent work, [PERSON] et al. [21] proposed the Faster R-CNN network in their fingertip detection and tracking system for air-writing recognition. The system was decomposed into several sections: detecting writing hand pose, keeping track of fingertips in every successive frame, and recognition of the air-writing characters. For hand pose detection, the Faster R-CNN network was trained on EgoFinger [22] and EgoHands datasets with four losses: RPN regression loss, RPN classification loss, R-CNN regression loss, and R-CNN classification loss. Other than focusing on detecting the hand pose in video streams, they also proposed implementing hand centroid localization to locate the position of the hands accurately. Visual trackers such as Kernelized Correlation Filter (KCF) tracker [23], Tracking-Learning-Detection (TLD) [24] tracker, and Multiple Instance Learning (MIL) [25] tracker were implemented as the hand tracking mechanism to assist the Faster R-CNN detector in keeping track with the fingertips movement during air-writing process. Their experimental results showed that the proposed method outperformed the rest of the visual trackers, achieving a mAP of 73.1% as compared with TLD tracker (mAP of 66.7%), KCF tracker (mAP of 55.4%), and MIL tracker (mAP of 42.4%). The focus of this work is to develop a fast and robust hand detection and tracking system that is reliable to support a real-time application. To achieve this objective, a tracking-by-detection algorithm is proposed by integrating the well-known correlation filter-based trackers, the KCF tracker [23], with the state-of-the-art deep learning object-detection algorithm, Single-Shot Detection (SSD) [26]. The KCF tracker is considered in this work because of its widespread acceptance due to its competitive performance in terms of speed and accuracy. For instance, the KCF tracker has been implemented in face tracking systems alongside a Continuously Adaptive Mean-Shift (CamShift) algorithm to optimize the tracking performance and to recover the tracker when the CamShift algorithm failed to track faces midway during inference [27]. Despite its promising characteristics, the KCF tracker still struggles to maintain seamless tracking in the occurrence of occlusion and objects falling out-of-view, and it is unable to correct errors during the tracking process [28].