## 2 Motivation
### Literature Review  
[PERSON] et al. (2018) emphasize the issue of identifying OOS items from a first-person perspective by introducing then overl method of annotating the Ego Cart dataset. Their strategy involves training a model to generate attention maps that can help detect OOS items in retail environments, enabling retailers to take the necessary actions. The results show that their approach has the potential to improve restocking processes in retail settings. The success of the method is significantly influenced by the level of accuracy of the annotations.  
[PERSON] and [PERSON] (2018) introduced a novel out-of-stock prediction system that utilizes deep learning and historical data to predict the remaining stock of retail products in the food distribution industry. The results of the experiments demonstrated the system's accuracy, with predictions exceeding 90%. Yet, there are still obstacles to overcome because of variations in the size, lighting, and background of areas where products are unavailable, which affect the precision of forecasts.  
[PERSON] and [PERSON] (2018) introduced a model for predicting stock crises using the Hybrid Feature Selection (HFS) technique. They first developed the HFS algorithm to eliminate unnecessary financial parameters and identify strong fundamental stocks to address overfitting problems. However, the model's performance was still lacking, necessitating further improvements and adjustments to the eXtreme Gradient Boosting (XG-Boost) method with a different optimizer. [PERSON] et al. (2018) performed research on forecasting stock market trends by utilizing machine learning and deep learning techniques. Initially, they calculated indicators based on continuous stock trading values and then converted these indicators into binary data for analysis. The results indicated that deep learning methods performed the best when using binary data for evaluation. However, the difference in performance between the methods lessened due to the significant improvement in model performance using the second approach.  
[PERSON] et al. (2018) suggest a new DL-driven technique for detecting out-of-distribution samples, using a two-step training approach and a post-processing method to filter out inaccurate detections. The two-step training process enhanced the models' ability to generalize and improved upon the baseline results. However, incorporating depth estimation into the current solution could further enhance the outcomes. [PERSON] (2018) introduced a novel stock market forecasting model with three key stages: extracting features, selecting optimal features, and making predictions. During neural network training, the model fine-tunes the optimal weights to achieve accurate predictions with high performance. However, the model encountered challenges related to overfitting. [PERSON] and [PERSON] (2018) introduced a technique for effectively monitoring shelves in retail establishments by utilizing supervised learning to enhance on-shelf availability. This method allows store employees to uphold a high level of on-shelf availability, thereby boosting profits for retail stores. Nonetheless, the challenge lies in the low resolution and frame rate of the videos, hindering the ability to track moving objects across successive images.