What makes for good multiple object trackers?
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- Type
- article
- Published
- 2016-10-01
- Cited by
- 7
- References
- 16
- OpenAlex
- https://openalex.org/W2594487602
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:6485037
Keywords
Artificial intelligence, Computer science, BitTorrent tracker, Computer vision, Histogram
References
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics
- Fast Feature Pyramids for Object Detection
- Caffe: Convolutional Architecture for Fast Feature Embedding
- ImageNet classification with deep convolutional neural networks
- Joint Probabilistic Data Association Revisited
- Visual Tracking with Fully Convolutional Networks
- Hierarchical Convolutional Features for Visual Tracking
- Learning to Track: Online Multi-object Tracking by Decision Making
- Multiple Hypothesis Tracking Revisited
- Simple online and realtime tracking
- The Hungarian method for the assignment problem
- MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
- Visualizing and Understanding Convolutional Networks
- A New Approach to Linear Filtering and Prediction Problems
Cited by
- Fast multi-object tracking using convolutional neural networks with tracklets updating
- Multi-object tracking using deformable convolution networks with tracklets updating
- Unsupervised pre-trained filter learning approach for efficient convolution neural network
- Real-Time Multiobject Tracking Based on Multiway Concurrency
- City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones
- Beyond Classifiers: Remote Sensing Change Detection with Metric Learning
- Real-Time Target Tracking Based on Airborne Vision
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