Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks (1602.00991v2)

Abstract: This paper presents to the best of our knowledge the first end-to-end object tracking approach which directly maps from raw sensor input to object tracks in sensor space without requiring any feature engineering or system identification in the form of plant or sensor models. Specifically, our system accepts a stream of raw sensor data at one end and, in real-time, produces an estimate of the entire environment state at the output including even occluded objects. We achieve this by framing the problem as a deep learning task and exploit sequence models in the form of recurrent neural networks to learn a mapping from sensor measurements to object tracks. In particular, we propose a learning method based on a form of input dropout which allows learning in an unsupervised manner, only based on raw, occluded sensor data without access to ground-truth annotations. We demonstrate our approach using a synthetic dataset designed to mimic the task of tracking objects in 2D laser data -- as commonly encountered in robotics applications -- and show that it learns to track many dynamic objects despite occlusions and the presence of sensor noise.

• The paper introduces an RNN-based tracking method that learns object dynamics directly from raw sensor data, bypassing manual feature engineering.
• It employs unsupervised input dropout to predict occluded object positions, achieving real-time processing at approximately 10ms per iteration.
• Experimental results on synthetic 2D laser data demonstrate its potential for autonomous driving and robotics in complex, dynamic environments.

Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

The paper, titled "Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks" by Peter Ondruska and Ingmar Posner, presents a novel method for object tracking that relies on recurrent neural networks (RNNs) to process raw sensor data. This approach circumvents the traditional reliance on hand-engineered features and system models, providing an end-to-end solution that maps sensor inputs directly to object tracks, including those that are occluded.

Overview and Approach

The researchers propose a deep learning framework that leverages RNNs to predict the location of objects, even in partially observable environments. This technique focuses on modeling sequences of sensor data to estimate a complete environmental state. The system is innovative as it eliminates the need for feature engineering by learning the dynamics of the environment directly from the data. The authors highlight the employment of a unique unsupervised learning strategy using input dropout, enabling the system to infer the state of occluded objects without ground-truth annotations.

The core technical contribution lies in the use of RNNs to model $P(y_t | x_{1:t})$, where $y_t$ represents the complete scene around the robot, and $x_t$ are the sensor observations. The paper frames object tracking as a recursive Bayesian estimation problem, traditionally challenging because scene elements might remain unobserved due to occlusion. Ondruska and Posner address this by training the network to predict future states, allowing it to learn and infer movements when objects aren't visible.

Key Results and Contributions

The authors deployed their method on synthetic datasets emulating 2D laser data to demonstrate the capability of their framework to track multiple dynamic objects in environments typical to robotics applications, despite noise and occlusions. The experimental results were significant, showing high fidelity in reconstructing the scene state in real-time applications.

Specifically:

• The system achieved real-time performance, processing data in approximately 10 ms per iteration on standard hardware.
• Unsupervised training facilitated the learning of object dynamics devoid of any explicit annotations, positing a major step forward for online learning in robotics.

Implications and Future Directions

This research opens various avenues for enhancing autonomous system perception by offering a fundamental shift from model-based to model-free tracking approaches. The implications are vast for sectors like autonomous driving, where anticipating occluded agents can drastically improve decision-making robustness and safety.

Looking ahead, practical applications of this method will require validation in real-world scenarios, which include more complex settings and integrating varied sensing modalities such as 3D lidar and depth cameras. These expansions could improve the predictive performance and offer rich contextual understanding in multi-agent and dynamic environments.

The successful adaptation of this method to real-time, real-world environments may also prompt further research into more sophisticated RNN architectures, like Long Short-Term Memory (LSTM) networks, which could enhance temporal feature extraction and improve predictiveness in more challenging environments.

Conclusion

The work by Ondruska and Posner importantly demonstrates the feasibility and potential of using deep learning for object tracking without engineered models or features. Their approach represents a step toward more autonomous systems capable of understanding and reacting to their environments effectively, underpinning the future of robotics and AI in dynamic, uncertain contexts. The innovative handling of occlusions and sensor noise, combined with the computational efficiency, highlights the significant contributions and the forward trajectory of this research.