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Deep Recurrent Neural Network for Multi-target Filtering
- 1. Deep Recurrent Neural Network for Multi-target Filtering Mehryar Emambakhsh and Alessandro Bay Cortexica Vision Systems London, UK January 11th, 2019 25th International Conference on MultiMedia Modeling Eduard Vazquez AnyVision Belfast, UK
- 2. Multi-target filtering: definition & applications ● A multi-target filtering algorithm removes clutter (false positive) from a sequential data. ● Examples: Stereo vision Radar/LiDAR signal analysis Robotics: SLAM and occupancy grid Object detection and tracking ● Example of multi-target filtering usage: varying detection threshold to increase TPR
- 3. Multi-target filtering: algorithms ● Kalman Filter (KF) based techniques: Gaussian and linear motion model assumptions. Extended KF: Linearisation via Taylor series expansion to maintain Gaussian behaviour Unscented KF: Deterministic selection of sample at different variances along each dimension to compute covariance and mean for an estimated Gaussian function. ● Information Filter (IF) Similar to KF, but works on canonical space (inverted covariance, i.e. information matrix) SEIF: unlike the covariance matrix, the information matrix is very sparse. SEIF uses this sparsity to discard significant number of landmarks at each iteration, improving computation time. ● Particle filter Estimates the posterior using Monte Carlo technique. Can handle non-linear non-Gaussian models. ● Neural networks: Recurrent neural networks Long short-term memory
- 4. Multi-target filtering: challenges ● Extension to multi-target: Random finite sets (RFS) and Probability Hypothesis Density (PHD) Mapping the multi-target state vectors to a universal single target problem ● Fixed motion model issues: Complex non-linear motion (can happen in presence of a noisy detector) can lead to wrong predictions. ● Gaussian assumption, especially in KF-PHD ● Challenges in using sequential learning algorithms: Unlike the Bayesian generative models, they rely on a separate train/test steps Cluttered unlabelled data can lead to weak predictive models Variable input size Memory management
- 5. Proposed multi-target filtering algorithm
- 6. Proposed algorithm ● The proposed algorithm addresses the following problems: Handing non-linear non-Gaussian multi-target motion Does not rely on a fixed motion model and it learns it incrementally Use of neural networks (an example of a sequential learning algorithm) for filtering
- 7. Proposed algorithm: prediction step ● A target tuple is defined as: ● The model initially is trained to act as an auto-encoder regressor. ● Once the model is trained, it is transferred to the other target, saving time and memory.
- 8. Proposed algorithm: data association & filtering ● Using the incoming measurement RFS a set of residual tuples are computed:
- 9. Proposed algorithm: data association & filtering ● Computing all the ‘targetness’ error T for all measurements and targets creates a matrix:
- 10. Proposed algorithm: Update ● Using the targetness matrix: ● ● ● ● ● ● ● ● ● Then the following data association algorithm is used assign target survival (true positivity), death (false positivity) and birth →
- 11. Proposed algorithm: Complexity analysis ● A basic Hungarian Matching: ● GM-PHD filter: ● The proposed method:
- 13. Multi-target filtering: experimental results ● The proposed algorithm is applied to a synthetic multi-target filtering scenario: Multiple scenarios are considered, such as: Variable number of targets Non-linear motion Birth/spawn of targets Dense random clutter with a Poisson distribution Noisy measurement Occlusion Merge of targets ● Temporal overlay visualisation of the filtering result
- 14. Multi-target filtering: experimental results
- 15. Multi-target filtering: experimental results ● Optimal Sub-Pattern Assignment (OSPA) is used as the quantitative metric.
- 16. Multi-target filtering: experimental results ● Robustness against clutter density:
- 17. Conclusions and future work ● An algorithm is proposed to address non-linearity and fixed motion model challenges of the available multi-target filtering algorithms. ● It is based on the use of a novel target tuple definition, LSTM architecture for motion modelling and a linearly complex data association step. ● Future work: Real data Other applications: tracking, detection, etc. End to end implementation
- 18. Thank you www.cortexica.com 3rd Floor – 30 Stamford Street WeWork Southbank Central London SE1 9LQ +44 (0) 203 868 8880 info@cortexica.com Twitter: @cortexica