## 2 Literature Review
They also proposed a spatio-temporal module to count the past and future features. They claimed better deblurring performance with less computational cost than the competing methods. A multi-stage system based on deep CNN detection and tracking system in videos for object detection was proposed by [PERSON] et al. [17] that constituted of two principal modules: firstly, a proposal module for tubelets that provided object detection, and the object tracking proposals for tubelets; secondly, a classification and scoring module for tubelets that provided max spatial pooling with robust box scoring, and time convolution to integrate temporal consistency. In this manner, the identification and monitoring of artifacts worked closely. On one hand, object detection provided high trust anchored to trigger tracking while simultaneously decreasing spatial tracking (max-pooling) failure. The monitoring also produced new object detection proposals, and the tracked boxes served as anchors for combining existing detections. A spatially supervised RNN was developed by [PERSON] et al. [18] for visual object tracking. They proposed the Recurrent you Only Look Once (ROLO) approach extended neural network research to the spatiotemporal domain. Their proposed tracker was both deep in space and time to tackle serious occlusive problems effectively with extreme motion blur. Based on a comparison with benchmark tracking datasets, the detailed experimental results showed that the proposed tracker was more robust, low computational cost, and accurate against competing methods. Similarly, [PERSON] et al. [19] proposed an offline neural network method to track new objects at 100 fps, an enormous frame rate, in tested time slots. [PERSON] et al. [20] used a hierarchical convolutional for tracking purposes using the visual features and found improvement in the system's robustness by representation learning for object recognition datasets. [PERSON], et al. [21] studied object tracking using full CNN and found that different levels of convolutional layers had different properties. To capture the semantic information of the target, and distinguish the target from background distractors, they opted for an integrated approach and considered the properties jointly. [PERSON] et al. [22] studied moving object detection in aerial video based on spatio-temporal saliency. They suggested a new method of spatiotemporal savings using the saliency technique for hierarchical movement of goal detection. The experimental results demonstrated that this method recognized high-precision and efficient moving substances in the airborne film. Furthermore, compared to the Motion History Image (MHI) technique, this method accompanied no time delay effect. Still, this approach measured the positions of the objects in any video frame as self-sufficient and unavoidable false alarms. [PERSON] et al. [23] introduced computation on unclear areas rejecting useless information following computational efficiency, namely coarse-to-fine object localization with object identification for Unmanned Armed Vehicles (UAV) imagery using lightweight CNN. This frame selection philosophy detects the objects coarsely, increasing detection speed and accuracy. They generated a deep motion saliency map for UAVs using LiteFlowNet, a lightweight CNN model, for refining the detection results [24] and extracted deep features through PeleeNets [25]. [PERSON] et al. [26] proposed a method for object detection for video frame tracking. The simulation result showed that this technique was efficient, precise, and robust for good performance of generic object class detection. [PERSON] et al. [27] suggested a method for text data detection based on big data analytics texturing in video frames. The video frames were decomposed into blocks (fixed size) and analyzed by Haar wavelet transformations followed by training of a neural network to distinguish blocks of text from non-text. [PERSON] et al.