【跟踪Tracking】15篇论文+代码 | 中秋快乐~

2018 年 9 月 24 日 专知
【跟踪Tracking】15篇论文+代码 | 中秋快乐~

【导读】中秋断更?不存在的。小编为大家整理了15篇GitHub上超100 stars的跟踪方向代码实现及相应论文,请各位读者笑纳。中秋快乐~



Tracking the World State with Recurrent Entity Networks

It is equipped with a dynamic long-term memory which allows it to maintain and update a representation of the state of the world as it receives new data. The EntNet sets a new state-of-the-art on the bAbI tasks, and is the first method to solve all the tasks in the 10k training examples setting.

代码:https://github.com/facebook/MemNN

论文:https://arxiv.org/abs/1612.03969v3




ArtTrack: Articulated Multi-person Tracking in the Wild

In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos. Our starting point is a model that resembles existing architectures for single-frame pose estimation but is substantially faster.

代码:https://github.com/eldar/pose-tensorflow

论文:https://arxiv.org/abs/1612.01465v3




Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking

In this paper, we develop a new approach of spatially supervised recurrent convolutional neural networks for visual object tracking. Our recurrent convolutional network exploits the history of locations as well as the distinctive visual features learned by the deep neural networks.

代码:https://github.com/Guanghan/ROLO

论文:https://arxiv.org/abs/1607.05781v1




Simple Online and Realtime Tracking with a Deep Association Metric

Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms. In this paper, we integrate appearance information to improve the performance of SORT.


代码:https://github.com/nwojke/deep_sort

论文:https://arxiv.org/abs/1703.07402v1




Simple Online and Realtime Tracking

This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications. To this end, detection quality is identified as a key factor influencing tracking performance, where changing the detector can improve tracking by up to 18.9%.

代码:https://github.com/abewley/sort

论文:https://arxiv.org/abs/1602.00763v2




Provable Dynamic Robust PCA or Robust Subspace Tracking

Dynamic robust PCA refers to the dynamic (time-varying) extension of robust PCA (RPCA). It assumes that the true (uncorrupted) data lies in a low-dimensional subspace that can change with time, albeit slowly.

代码:https://github.com/andrewssobral/lrslibrary

论文:https://arxiv.org/abs/1705.08948v4




Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters

With the rise of end-to-end learning through deep learning, person detectors and re-identification (ReID) models have recently become very strong. Multi-camera multi-target (MCMT) tracking has not fully gone through this transformation yet.


代码:

https://github.com/VisualComputingInstitute/triplet-reid

论文:https://arxiv.org/abs/1705.04608v2




Learning Multi-Domain Convolutional Neural Networks for Visual Tracking

Our algorithm pretrains a CNN using a large set of videos with tracking ground-truths to obtain a generic target representation. Online tracking is performed by evaluating the candidate windows randomly sampled around the previous target state.

代码:https://github.com/HyeonseobNam/MDNet

论文:https://arxiv.org/abs/1510.07945v2




ECO: Efficient Convolution Operators for Tracking

In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. Moreover, our fast variant, using hand-crafted features, operates at 60 Hz on a single CPU, while obtaining 65.0% AUC on OTB-2015.

代码:https://github.com/martin-danelljan/ECO

论文:https://arxiv.org/abs/1611.09224v2




Markerless tracking of user-defined features with deep learning

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming.


代码:

https://github.com/AlexEMG/DeepLabCut

论文:https://arxiv.org/abs/1804.03142v1




Distractor-aware Siamese Networks for Visual Object Tracking

In this paper, we focus on learning distractor-aware Siamese networks for accurate and long-term tracking. During the off-line training phase, an effective sampling strategy is introduced to control this distribution and make the model focus on the semantic distractors.

代码:https://github.com/foolwood/DaSiamRPN

论文:https://arxiv.org/abs/1808.06048v1




DCFNet: Discriminant Correlation Filters Network for Visual Tracking

Discriminant Correlation Filters (DCF) based methods now become a kind of dominant approach to online object tracking. In this work, we present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously.

代码:https://github.com/foolwood/DCFNet

论文:https://arxiv.org/abs/1704.04057v1




A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"

Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos.

代码:https://github.com/zhusz/CVPR15-CFSS

论文:https://arxiv.org/abs/1603.06015v2




Hierarchical Attentive Recurrent Tracking

Class-agnostic object tracking is particularly difficult in cluttered environments as target specific discriminative models cannot be learned a priori. Inspired by how the human visual cortex employs spatial attention and separate "where" and "what" processing pathways to actively suppress irrelevant visual features, this work develops a hierarchical attentive recurrent model for single object tracking in videos.

代码:https://github.com/akosiorek/hart

论文:https://arxiv.org/abs/1706.09262v2




Real-Time Multiple Object Tracking - A Study on the Importance of Speed

In this project, we implement a multiple object tracker, following the tracking-by-detection paradigm, as an extension of an existing method. It works by modelling the movement of objects by solving the filtering problem, and associating detections with predicted new locations in new frames using the Hungarian algorithm.

代码:https://github.com/samuelmurray/tracking-by-detection

论文:https://arxiv.org/abs/1709.03572v2


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Data association-based multiple object tracking (MOT) involves multiple separated modules processed or optimized differently, which results in complex method design and requires non-trivial tuning of parameters. In this paper, we present an end-to-end model, named FAMNet, where Feature extraction, Affinity estimation and Multi-dimensional assignment are refined in a single network. All layers in FAMNet are designed differentiable thus can be optimized jointly to learn the discriminative features and higher-order affinity model for robust MOT, which is supervised by the loss directly from the assignment ground truth. We also integrate single object tracking technique and a dedicated target management scheme into the FAMNet-based tracking system to further recover false negatives and inhibit noisy target candidates generated by the external detector. The proposed method is evaluated on a diverse set of benchmarks including MOT2015, MOT2017, KITTI-Car and UA-DETRAC, and achieves promising performance on all of them in comparison with state-of-the-arts.

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Model update lies at the heart of object tracking.Generally, model update is formulated as an online learning problem where a target model is learned over the online training dataset. Our key innovation is to \emph{learn the online learning algorithm itself using large number of offline videos}, i.e., \emph{learning to update}. The learned updater takes as input the online training dataset and outputs an updated target model. As a first attempt, we design the learned updater based on recurrent neural networks (RNNs) and demonstrate its application in a template-based tracker and a correlation filter-based tracker. Our learned updater consistently improves the base trackers and runs faster than realtime on GPU while requiring small memory footprint during testing. Experiments on standard benchmarks demonstrate that our learned updater outperforms commonly used update baselines including the efficient exponential moving average (EMA)-based update and the well-designed stochastic gradient descent (SGD)-based update. Equipped with our learned updater, the template-based tracker achieves state-of-the-art performance among realtime trackers on GPU.

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Object tracking is challenging as target objects often undergo drastic appearance changes over time. Recently, adaptive correlation filters have been successfully applied to object tracking. However, tracking algorithms relying on highly adaptive correlation filters are prone to drift due to noisy updates. Moreover, as these algorithms do not maintain long-term memory of target appearance, they cannot recover from tracking failures caused by heavy occlusion or target disappearance in the camera view. In this paper, we propose to learn multiple adaptive correlation filters with both long-term and short-term memory of target appearance for robust object tracking. First, we learn a kernelized correlation filter with an aggressive learning rate for locating target objects precisely. We take into account the appropriate size of surrounding context and the feature representations. Second, we learn a correlation filter over a feature pyramid centered at the estimated target position for predicting scale changes. Third, we learn a complementary correlation filter with a conservative learning rate to maintain long-term memory of target appearance. We use the output responses of this long-term filter to determine if tracking failure occurs. In the case of tracking failures, we apply an incrementally learned detector to recover the target position in a sliding window fashion. Extensive experimental results on large-scale benchmark datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods in terms of efficiency, accuracy, and robustness.

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Template-matching methods for visual tracking have gained popularity recently due to their comparable performance and fast speed. However, they lack effective ways to adapt to changes in the target object's appearance, making their tracking accuracy still far from state-of-the-art. In this paper, we propose a dynamic memory network to adapt the template to the target's appearance variations during tracking. An LSTM is used as a memory controller, where the input is the search feature map and the outputs are the control signals for the reading and writing process of the memory block. As the location of the target is at first unknown in the search feature map, an attention mechanism is applied to concentrate the LSTM input on the potential target. To prevent aggressive model adaptivity, we apply gated residual template learning to control the amount of retrieved memory that is used to combine with the initial template. Unlike tracking-by-detection methods where the object's information is maintained by the weight parameters of neural networks, which requires expensive online fine-tuning to be adaptable, our tracker runs completely feed-forward and adapts to the target's appearance changes by updating the external memory. Moreover, the capacity of our model is not determined by the network size as with other trackers -- the capacity can be easily enlarged as the memory requirements of a task increase, which is favorable for memorizing long-term object information. Extensive experiments on OTB and VOT demonstrates that our tracker MemTrack performs favorably against state-of-the-art tracking methods while retaining real-time speed of 50 fps.

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Discrete correlation filter (DCF) based trackers have shown considerable success in visual object tracking. These trackers often make use of low to mid level features such as histogram of gradients (HoG) and mid-layer activations from convolution neural networks (CNNs). We argue that including semantically higher level information to the tracked features may provide further robustness to challenging cases such as viewpoint changes. Deep salient object detection is one example of such high level features, as it make use of semantic information to highlight the important regions in the given scene. In this work, we propose an improvement over DCF based trackers by combining saliency based and other features based filter responses. This combination is performed with an adaptive weight on the saliency based filter responses, which is automatically selected according to the temporal consistency of visual saliency. We show that our method consistently improves a baseline DCF based tracker especially in challenging cases and performs superior to the state-of-the-art. Our improved tracker operates at 9.3 fps, introducing a small computational burden over the baseline which operates at 11 fps.

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