Trajectory Prediction using Memory Networks: A Comprehensive Review

Trajectory prediction is a crucial task in various fields such as autonomous driving, robotics, and computer vision. Accurately forecasting future trajectories can help prevent accidents and improve overall performance. Recently, memory networks have gained popularity in trajectory prediction tasks due to their ability to capture complex relationships between past and future events.
In this article, we will delve into the concept of memory networks and their application in trajectory prediction. We will explore how these networks store information from the past and use it to predict future trajectories. We will also examine different approaches to creating memory repositories and utilizing multimodal information for human trajectory prediction.
One approach to trajectory prediction is to use generative models, which generate a wide range of potential future trajectories. However, these models struggle to accurately forecast specific trajectories in various situations. To overcome this challenge, researchers have turned to memory networks, which can store information from the past and use it to predict future events.
Memory networks are based on the idea of a hierarchical neural network that stores information in different levels of abstraction. Each level of abstraction represents a different aspect of the data, such as individual features or patterns. By storing information at multiple levels of abstraction, memory networks can capture complex relationships between past and future events.
Several methods have established memory repositories with diverse contents. For example, some methods store all possible trajectories in the memory repository, while others focus on specific aspects of the data, such as human movements. By clustering trajectories within the same scene and filtering them based on similarity, these methods can utilize multimodal information for human trajectory prediction.
Another approach to memory networks is to use a scoring network to predict target points. This approach focuses on training a network to predict the likelihood of each point in the future based on its past movements. By using this method, memoNet can accurately forecast future locations and velocities.
In conclusion, memory networks have proven to be a valuable tool for trajectory prediction tasks. They can capture complex relationships between past and future events and accurately forecast specific trajectories in various situations. As the field of computer vision continues to evolve, we can expect to see further advancements in memory network-based trajectory prediction methods.