Multi-Camera Trajectory Forecasting with Trajectory Tensors
Learn to forecast trajectories of moving objects across multiple cameras using Trajectory Tensors, enhancing surveillance and traffic monitoring applications
- Define the problem of multi-camera trajectory forecasting (MCTF) and its challenges
- Build a Trajectory Tensor representation to model the movement of objects across cameras
- Configure a deep learning model to predict future trajectories using the Trajectory Tensor
- Test the model on a multi-camera dataset to evaluate its performance
- Apply the MCTF method to real-world applications such as surveillance and traffic monitoring
Computer vision engineers and researchers working on surveillance and traffic monitoring systems can benefit from this technique to improve their trajectory forecasting capabilities
💡 Trajectory Tensors can effectively model the movement of objects across multiple cameras, enabling accurate trajectory forecasting
Forecast trajectories across multiple cameras with Trajectory Tensors! #computerVision #surveillance
Key Takeaways
Learn to forecast trajectories of moving objects across multiple cameras using Trajectory Tensors, enhancing surveillance and traffic monitoring applications
Full Article
Abstract:
arXiv:2108.04694v2 Announce Type: cross Abstract: We introduce the problem of multi-camera trajectory forecasting (MCTF), which involves predicting the trajectory of a moving object across a network of cameras. While multi-camera setups are widespread for applications such as surveillance and traffic monitoring, existing trajectory forecasting methods typically focus on single-camera trajectory forecasting (SCTF), limiting their use for such applications. Furthermore, using a single camera limit
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