Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection (1702.05552v1)

Abstract: As humans we possess an intuitive ability for navigation which we master through years of practice; however existing approaches to model this trait for diverse tasks including monitoring pedestrian flow and detecting abnormal events have been limited by using a variety of hand-crafted features. Recent research in the area of deep-learning has demonstrated the power of learning features directly from the data; and related research in recurrent neural networks has shown exemplary results in sequence-to-sequence problems such as neural machine translation and neural image caption generation. Motivated by these approaches, we propose a novel method to predict the future motion of a pedestrian given a short history of their, and their neighbours, past behaviour. The novelty of the proposed method is the combined attention model which utilises both "soft attention" as well as "hard-wired" attention in order to map the trajectory information from the local neighbourhood to the future positions of the pedestrian of interest. We illustrate how a simple approximation of attention weights (i.e hard-wired) can be merged together with soft attention weights in order to make our model applicable for challenging real world scenarios with hundreds of neighbours. The navigational capability of the proposed method is tested on two challenging publicly available surveillance databases where our model outperforms the current-state-of-the-art methods. Additionally, we illustrate how the proposed architecture can be directly applied for the task of abnormal event detection without handcrafting the features.

• The paper introduces a novel LSTM model combining soft and hardwired attention to enhance trajectory prediction accuracy.
• It outperforms state-of-the-art methods like Social-LSTM on ADE, FDE, and n-ADE metrics using real-world datasets.
• The framework efficiently detects abnormal events, offering practical benefits in surveillance and urban planning.

Overview of "Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection"

The paper "Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection" by Fernando et al. introduces a novel approach to model pedestrian trajectories, leveraging the strengths of Long Short-Term Memory (LSTM) networks with a dual attention mechanism. The methodology outlined in the paper focuses on enhancing pedestrian trajectory prediction and abnormal event detection within crowded environments, aiming to improve upon traditional methods that rely heavily on handcrafted features.

Proposed Model

The model integrates two forms of attention: "soft attention," which dynamically learns the importance of different segments of the trajectory data using backpropagation, and "hardwired attention," which is specifically designed to incorporate spatial contextual information via distance-based weighting. The combination of these attention mechanisms enables the system to efficiently manage complex data interactions, particularly in environments with high pedestrian densities. The paper emphasizes that by integrating both local neighborhood dynamics and historical data of the pedestrian of interest, the system can generate more accurate future trajectory predictions.

Experimental Results

The authors demonstrate the applicability and efficacy of their model on two well-known datasets: the New York Grand Central and the Edinburgh Informatics Forum databases. The experiments reveal that the proposed model outperforms state-of-the-art methods including the Social-LSTM and Social Force models in terms of several key performance metrics such as Average Displacement Error (ADE), Final Displacement Error (FDE), and Average Non-linear Displacement Error (n-ADE).

The results are particularly notable in crowded settings, where the dual attention model captures and integrates both immediate interactions and broader trajectory trends. This enables the model to make more informed predictions compared to conventional approaches, which may rely solely on immediate past observations. The clustering of trajectories based on entry and exit points further refines the accuracy by taking into account distinct behavioral patterns evident in specific spatial contexts.

Implications and Future Work

The implications of this research are manifold. Practically, the ability to accurately predict pedestrian movements can enhance the design of intelligent surveillance systems and urban planning efforts focused on pedestrian traffic management. The application for abnormal event detection is particularly significant, as it eliminates the need for handcrafted feature engineering by leveraging LSTM hidden states to identify anomalies.

Theoretically, the combination of soft and hardwired attention mechanisms proposes an intriguing direction for enhancing RNN-based models. Future research could explore extending these concepts to other domains involving sequential data, such as vehicle trajectory modeling or more complex multi-agent systems. Furthermore, investigating the scalability of this approach in real-time applications and its adaptation to other forms of non-traditional data streams could provide valuable insights.

In conclusion, Fernando et al. present a compelling evolution of trajectory prediction models that successfully balances computational efficiency and prediction accuracy through a hybrid attention approach. As AI continues to advance, the insights offered by this work provide a robust foundation for future exploration in complex systems where agent interactions are richly layered and dynamic.