Deep Learning for Vision-based Prediction: A Survey (2007.00095v2)

Abstract: Vision-based prediction algorithms have a wide range of applications including autonomous driving, surveillance, human-robot interaction, weather prediction. The objective of this paper is to provide an overview of the field in the past five years with a particular focus on deep learning approaches. For this purpose, we categorize these algorithms into video prediction, action prediction, trajectory prediction, body motion prediction, and other prediction applications. For each category, we highlight the common architectures, training methods and types of data used. In addition, we discuss the common evaluation metrics and datasets used for vision-based prediction tasks. A database of all the information presented in this survey including, cross-referenced according to papers, datasets and metrics, can be found online at https://github.com/aras62/vision-based-prediction.

• The paper systematically reviews deep learning techniques applied to vision-based prediction across key categories including video, action, trajectory, and motion.
• It highlights architectures like RNNs, CNNs, GANs, and VAEs along with performance metrics such as PSNR, SSIM, ADE, and FDE.
• The survey identifies challenges in occlusion handling and metric standardization while suggesting future improvements for real-world applications.

Overview of "Deep Learning for Vision-based Prediction: A Survey"

The paper "Deep Learning for Vision-based Prediction: A Survey" provides an in-depth analysis of vision-based prediction algorithms with an emphasis on those utilizing deep learning methodologies developed over the past five years. These algorithms largely benefit applications like autonomous driving, surveillance, human-robot interaction, and weather forecasting. The survey methodically categorizes these vision-based prediction algorithms into five primary subdivisions: video prediction, action prediction, trajectory prediction, motion prediction, and miscellaneous applications.

Key Categories and Methodologies

1. Video Prediction: Video prediction algorithms attempt to forecast future scenes, often encapsulated as RGB images or optical flow maps, using historical observations. They commonly employ recurrent architectures, such as LSTMs and GRUs, and generative frameworks, including GANs and VAEs, to address the challenges related to capturing temporal dynamics and spatial vanishing interactions. Numerical results showcased involve advanced architectures leading to promising results under controlled conditions, whereas handling occlusions in complex scenes remains a critical task to refine.
2. Action Prediction: Divided into action anticipation and early action prediction, these approaches mostly use RNN-based frameworks to predict either future actions or complete actions prematurely based on partial observations. Certain methods extend beyond RNNs to incorporate 2D/3D CNNs for processing visual inputs from diverse modalities, enhancing capability in complex environments like traffic scenarios.
3. Trajectory Prediction: This section focuses on forecasting the future paths of dynamic entities, with an underlying reliance on past trajectory data and contextual information. Recurrent networks are prominently preferred for modeling social interactions and human movement versatility. The ability to anticipate trajectories in traffic settings, employing scene layouts and vehicle dynamics, is particularly highlighted, with existing models overcoming several classical limitations due to deep learning enhancements.
4. Motion Prediction: Predominantly concentrating on human pose progression, these studies capitalize on recurrent or feedforward architectures for modeling sequential body joint movements. Despite the efficacy observed in rehearsal and limited prediction windows, challenges persist in integrating contextual and interactive elements that influence movement dynamics.
5. Other Applications: This category encapsulates efforts such as map prediction, semantic segmentation, and nuanced applications, including trend and contest outcome predictions. The paper outlines how innovative deep learning solutions are tailored to unique modality constraints and demands prominent in each specific application domain.

Evaluation Metrics and Datasets

Evaluating these algorithms involves various domain-specific metrics such as MSE, PSNR, SSIM for video quality; accuracy, precision, recall for classification tasks; and ADE, FDE for trajectory forecasting. The survey underscores the lack of metric standardization, particularly in trajectory prediction, leading to potential misinterpretations across studies. Datasets employed across categories span publicly available repositories like ETH and UCY for trajectories, JAAD for action prediction, and Human 3.6M for motion, each offering diverse data types from RGB images to LIDAR point clouds.

Implications and Future Research Directions

This paper not only consolidates current advancements but also identifies gaps and future scopes in vision-based prediction. It points out prevalent challenges such as accurate prediction under occlusion, dynamic scene complexity, and contextual reasoning integration. Furthermore, it encourages a systematic evaluation through benchmark datasets and a common metric framework to enhance comparability across works, which is essential given the disparities currently observed.

The growth in this research space anticipates collaborative improvements across methodologies, dataset curation, and application breadth, enabled by innovations in network architectures, attention mechanisms, and multi-modal data assimilation. Future investigations are likely to further integrate real-time adaptability and scalability into these predictive models to address inherently stochastic and safety-critical scenarios effectively.