Online multi-object tracking is a fundamental problem in time-critical video analysis applications. A major challenge in the popular tracking-by-detection framework is how to associate unreliable detection results with existing tracks. In this paper, we propose to handle unreliable detection by collecting candidates from outputs of both detection and tracking. The intuition behind generating redundant candidates is that detection and tracks can complement each other in different scenarios. Detection results of high confidence prevent tracking drifts in the long term, and predictions of tracks can handle noisy detection caused by occlusion. In order to apply optimal selection from a considerable amount of candidates in real-time, we present a novel scoring function based on a fully convolutional neural network, that shares most computations on the entire image. Moreover, we adopt a deeply learned appearance representation, which is trained on large-scale person re-identification datasets, to improve the identification ability of our tracker. Extensive experiments show that our tracker achieves real-time and state-of-the-art performance on a widely used people tracking benchmark.
Tracking-by-detection is becoming the most popular strategy for multi-object tracking. @cite_20 associated tracklets with detection in different ways according to their confidence values. Sanchez- @cite_12 exploited multiple detectors to improve tracking performance. They collected outputs from multiple detectors, during the so called over-detection process. Combining results from multiple detectors can improve the tracking performance but is not efficient for real-time applications. In contrast, our tracking framework needs only one detector and generates candidates from existing tracks. @cite_18 used a binary classifier and single object tracker for online multi-object tracking. They shared the feature maps for classification but still had a high computation complicity.
@cite_18 @cite_12 @cite_20
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Online multi-object tracking aims at producing complete tracks of multiple objects using the information accumulated up to the present moment. It still remains a difficult problem in complex scenes, because of frequent occlusion by clutter or other objects, similar appearances of different objects, and other factors. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first propose the tracklet confidence using the detectability and continuity of a tracklet, and formulate a multi-object tracking problem based on the tracklet confidence. The multi-object tracking problem is then solved by associating tracklets in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive associations. Here, for reliable association between tracklets and detections, we also propose a novel online learning method using an incremental linear discriminant analysis for discriminating the appearances of objects. By exploiting the proposed learning method, tracklet association can be successfully achieved even under severe occlusion. Experiments with challenging public datasets show distinct performance improvement over other batch and online tracking methods. We propose a tracking framework that mediates grouping cues from two levels of tracking granularities, detection tracklets and point trajectories, for segmenting objects in crowded scenes. Detection tracklets capture objects when they are mostly visible. They may be sparse in time, may miss partially occluded or deformed objects, or contain false positives. Point trajectories are dense in space and time. Their affinities integrate long range motion and 3D disparity information, useful for segmentation. Affinities may leak though across similarly moving objects, since they lack model knowledge. We establish one trajectory and one detection tracklet graph, encoding grouping affinities in each space and associations across. Two-granularity tracking is cast as simultaneous detection tracklet classification and clustering (cl2) in the joint space of tracklets and trajectories. We solve cl2 by explicitly mediating contradictory affinities in the two graphs: Detection tracklet classification modifies trajectory affinities to reflect object specific dis-associations. Non-accidental grouping alignment between detection tracklets and trajectory clusters boosts or rejects corresponding detection tracklets, changing accordingly their classification.We show our model can track objects through sparse, inaccurate detections and persistent partial occlusions. It adapts to the changing visibility masks of the targets, in contrast to detection based bounding box trackers, by effectively switching between the two granularities according to object occlusions, deformations and background clutter. Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking. This paper addresses the problem of simultaneous tracking of multiple targets in a video. We first apply object detectors to every video frame. Pairs of detection responses from every two consecutive frames are then used to build a graph of tracklets. The graph helps transitively link the best matching tracklets that do not violate hard and soft contextual constraints between the resulting tracks. We prove that this data association problem can be formulated as finding the maximum-weight independent set (MWIS) of the graph. We present a new, polynomial-time MWIS algorithm, and prove that it converges to an optimum. Similarity and contextual constraints between object detections, used for data association, are learned online from object appearance and motion properties. Long-term occlusions are addressed by iteratively repeating MWIS to hierarchically merge smaller tracks into longer ones. Our results demonstrate advantages of simultaneously accounting for soft and hard contextual constraints in multitarget tracking. We outperform the state of the art on the benchmark datasets. This paper presents an online detection-based two-stage multi-object tracking method in dense visual surveillances scenarios with a single camera. In the local stage, a particle filter with observer selection that could deal with partial object occlusion is used to generate a set of reliable tracklets. In the global stage, the detection responses are collected from a temporal sliding window to deal with ambiguity caused by full object occlusion to generate a set of potential tracklets. The reliable tracklets generated in the local stage and the potential tracklets generated within the temporal sliding window are associated by Hungarian algorithm on a modified pairwise tracklets association cost matrix to get the global optimal association. This method is applied to the pedestrian class and evaluated on two challenging datasets. The experimental results prove the effectiveness of our method. Robust multi-object tracking-by-detection requires the correct assignment of noisy detection results to object trajectories. We address this problem by proposing an online approach based on the observation that object detectors primarily fail if objects are significantly occluded. In contrast to most existing work, we only rely on geometric information to efficiently overcome detection failures. In particular, we exploit the spatio-temporal evolution of occlusion regions, detector reliability, and target motion prediction to robustly handle missed detections. In combination with a conservative association scheme for visible objects, this allows for real-time tracking of multiple objects from a single static camera, even in complex scenarios. Our evaluations on publicly available multi-object tracking benchmark datasets demonstrate favorable performance compared to the state-of-the-art in online and offline multi-object tracking. Online Multiple Target Tracking (MTT) is often addressed within the tracking-by-detection paradigm. Detections are previously extracted independently in each frame and then objects trajectories are built by maximizing specifically designed coherence functions. Nevertheless, ambiguities arise in presence of occlusions or detection errors. In this paper we claim that the ambiguities in tracking could be solved by a selective use of the features, by working with more reliable features if possible and exploiting a deeper representation of the target only if necessary. To this end, we propose an online divide and conquer tracker for static camera scenes, which partitions the assignment problem in local subproblems and solves them by selectively choosing and combining the best features. The complete framework is cast as a structural learning task that unifies these phases and learns tracker parameters from examples. Experiments on two different datasets highlights a significant improvement of tracking performances (MOTA +10 ) over the state of the art. Online Multiple Target Tracking (MTT) is often addressed within the tracking-by-detection paradigm. Detections are previously extracted independently in each frame and then objects trajectories are built by maximizing specifically designed coherence functions. Nevertheless, ambiguities arise in presence of occlusions or detection errors. In this paper we claim that the ambiguities in tracking could be solved by a selective use of the features, by working with more reliable features if possible and exploiting a deeper representation of the target only if necessary. To this end, we propose an online divide and conquer tracker for static camera scenes, which partitions the assignment problem in local subproblems and solves them by selectively choosing and combining the best features. The complete framework is cast as a structural learning task that unifies these phases and learns tracker parameters from examples. Experiments on two different datasets highlights a significant improvement of tracking performances (MOTA +10 ) over the state of the art. In this paper, we consider a multiobject tracking problem in complex scenes. Unlike batch tracking systems using detections of the entire sequence, we propose a novel online multiobject tracking system in order to build tracks sequentially using online provided detections. To track objects robustly even under frequent occlusions, the proposed system consists of three main parts: 1) visual tracking with a novel data association with a track existence probability by associating online detections with the corresponding tracks under partial occlusions; 2) track management to associate terminated tracks for linking tracks fragmented by long-term occlusions; and 3) online model learning to generate discriminative appearance models for successful associations in other two parts. Experimental results using challenging public data sets show the obvious performance improvement of the proposed system, compared with other state-of-the-art tracking systems. Furthermore, extensive performance analysis of the three main parts demonstrates effects and usefulness of the each component for multiobject 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 . Despite only using a rudimentary combination of familiar techniques such as the Kalman Filter and Hungarian algorithm for the tracking components, this approach achieves an accuracy comparable to state-of-the-art online trackers. Furthermore, due to the simplicity of our tracking method, the tracker updates at a rate of 260 Hz which is over 20x faster than other state-of-the-art trackers. We present a novel approach for multi-object tracking which considers object detection and spacetime trajectory estimation as a coupled optimization problem. Our approach is formulated in a minimum description length hypothesis selection framework, which allows our system to recover from mismatches and temporarily lost tracks. Building upon a state-of-the-art object detector, it performs multiview multicategory object recognition to detect cars and pedestrians in the input images. The 2D object detections are checked for their consistency with (automatically estimated) scene geometry and are converted to 3D observations which are accumulated in a world coordinate frame. A subsequent trajectory estimation module analyzes the resulting 3D observations to find physically plausible spacetime trajectories. Tracking is achieved by performing model selection after every frame. At each time instant, our approach searches for the globally optimal set of spacetime trajectories which provides the best explanation for the current image and for all evidence collected so far while satisfying the constraints that no two objects may occupy the same physical space nor explain the same image pixels at any point in time. Successful trajectory hypotheses are then fed back to guide object detection in future frames. The optimization procedure is kept efficient through incremental computation and conservative hypothesis pruning. We evaluate our approach on several challenging video sequences and demonstrate its performance on both a surveillance-type scenario and a scenario where the input videos are taken from inside a moving vehicle passing through crowded city areas. In this paper, we propose an online Multi-Object Tracking (MOT) approach which integrates the merits of single object tracking and data association methods in a unified framework to handle noisy detections and frequent interactions between targets. Specifically, for applying single object tracking in MOT, we introduce a cost-sensitive tracking loss based on the state-of-the-art visual tracker, which encourages the model to focus on hard negative distractors during online learning. For data association, we propose Dual Matching Attention Networks (DMAN) with both spatial and temporal attention mechanisms. The spatial attention module generates dual attention maps which enable the network to focus on the matching patterns of the input image pair, while the temporal attention module adaptively allocates different levels of attention to different samples in the tracklet to suppress noisy observations. Experimental results on the MOT benchmark datasets show that the proposed algorithm performs favorably against both online and offline trackers in terms of identity-preserving metrics. Although ‘tracking-by-detection’ is a popular approach when reliable object detectors are available, missed detections remain a difficult hurdle to overcome. We present a hybrid stochastic deterministic optimization scheme that uses RJMCMC to perform stochastic search over the space of detection configurations, interleaved with deterministic computation of the optimal multi-frame data association for each proposed detection hypothesis. Since object trajectories do not need to be estimated directly by the sampler, our approach is more efficient than traditional MCMCDA techniques. Moreover, our holistic formulation is able to generate longer, more reliable trajectories than baseline tracking-by-detection approaches in challenging multi-target scenarios. This paper presents an online multiple object tracking (MOT) method based on tracking by detection. Tracking by detection has the inherent problems by false and miss detection. To deal with the false detection, we employed the Gaussian mixture probability hypothesis density (GM-PHD) filter because this filter is robust to noisy and random data processing containing many false observations. Thus, we revised the GM-PHD filter for visual MOT. Also, to handle miss detection, we propose a hierarchical tracking framework to associate fragmented or ID switched tracklets. Experiments with the representative dataset PETS 2009 S2L1 show that our framework are effective to decrease the errors by false and miss detection, and real-time capability. We propose an online multiple object tracking algorithm that exploits optical flow and convolutional features to handle noisy detections as well as frequent occlusion. To achieve robust tracking, we develop a data association method that deals with tracking scenarios of increasing difficulty. For easy scenarios, we use motion affinity to associate detections with objects. For ambiguous situations, we propose to use an appearance model based on convolutional features and correlation filters to complement template matching methods. For difficult cases where objects are under heavy occlusion, we carry out occlusion analysis, which exploits the relationship between targets and occluders to predict potential object locations. To deal with noisy detections, false positives are detected and removed on both raw detection and tracklet levels, while missing and inaccurate detections are recovered or corrected via short-term tracking. Experimental results on two benchmark datasets demonstrate that the proposed online algorithm performs favorably against the state-of-the-art methods. For object detection, the two-stage approach (e.g., Faster R-CNN) has been achieving the highest accuracy, whereas the one-stage approach (e.g., SSD) has the advantage of high efficiency. To inherit the merits of both while overcoming their disadvantages, in this paper, we propose a novel single-shot based detector, called RefineDet, that achieves better accuracy than two-stage methods and maintains comparable efficiency of one-stage methods. RefineDet consists of two inter-connected modules, namely, the anchor refinement module and the object detection module. Specifically, the former aims to (1) filter out negative anchors to reduce search space for the classifier, and (2) coarsely adjust the locations and sizes of anchors to provide better initialization for the subsequent regressor. The latter module takes the refined anchors as the input from the former to further improve the regression accuracy and predict multi-class label. Meanwhile, we design a transfer connection block to transfer the features in the anchor refinement module to predict locations, sizes and class labels of objects in the object detection module. The multitask loss function enables us to train the whole network in an end-to-end way. Extensive experiments on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO demonstrate that RefineDet achieves state-of-the-art detection accuracy with high efficiency. Code is available at https: github.com sfzhang15 RefineDet. In this paper, we propose a label propagation framework to handle the multiple object tracking (MOT) problem for a generic object type (cf. pedestrian tracking). Given a target object by an initial bounding box, all objects of the same type are localized together with their identities. We treat this as a problem of propagating bi-labels, i.e. a binary class label for detection and individual object labels for tracking. To propagate the class label, we adopt clustered Multiple Task Learning (cMTL) while enforcing spatio-temporal consistency and show that this improves the performance when given limited training data. To track objects, we propagate labels from trajectories to detections based on affinity using appearance, motion, and context. Experiments on public and challenging new sequences show that the proposed method improves over the current state of the art on this task. For object detection, the two-stage approach (e.g., Faster R-CNN) has been achieving the highest accuracy, whereas the one-stage approach (e.g., SSD) has the advantage of high efficiency. To inherit the merits of both while overcoming their disadvantages, in this paper, we propose a novel single-shot based detector, called RefineDet, that achieves better accuracy than two-stage methods and maintains comparable efficiency of one-stage methods. RefineDet consists of two inter-connected modules, namely, the anchor refinement module and the object detection module. Specifically, the former aims to (1) filter out negative anchors to reduce search space for the classifier, and (2) coarsely adjust the locations and sizes of anchors to provide better initialization for the subsequent regressor. The latter module takes the refined anchors as the input from the former to further improve the regression and predict multi-class label. Meanwhile, we design a transfer connection block to transfer the features in the anchor refinement module to predict locations, sizes and class labels of objects in the object detection module. The multi-task loss function enables us to train the whole network in an end-to-end way. Extensive experiments on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO demonstrate that RefineDet achieves state-of-the-art detection accuracy with high efficiency. Code is available at this https URL This paper proposes a multiple-object detection and tracking method that explicitly handles dynamic occlusions. A context-based multiple-cue detector is proposed to detect occluded vehicles (occludees). First, we detect and track fully-visible vehicles (occluders). Occludee detection adopts those occluders as priors. Two classifiers for partially-visible vehicles are trained to use appearance cues. Disparity is adopted to further constrain the occludee locations. A detected occludee is then tracked by a Kalman-based tracking-by-detection method. As dynamic occlusions lead to role changes for occluder or occludee, an integrative module is introduced for possibly switching occludee and occluder trackers. The proposed system was tested on overtaking scenarios. It improved an occluder-only tracking system by over 10 regarding the frame-based detection rate, and by over 20 regarding the trajectory detection rate. The occludees are detected and tracked in the proposed method up to 7 seconds before they are picked up by occluder-only method. Recent work on multi-object tracking has shown the promise of tracklet-based methods. In this work we present a method which infers tracklets then groups them into tracks. It overcomes some of the disadvantages of existing methods, such as the use of heuristics or non-realistic constraints. The main idea is to formulate the data association problem as inference in a set of Bayesian networks. This avoids exhaustive evaluation of data association hypotheses, provides a confidence estimate of the solution, and handles split-merge observations. Consistency of motion and appearance is the driving force behind finding the MAP data association estimate. The computed tracklets are then used in a complete multi-object tracking algorithm, which is evaluated on a vehicle tracking task in an aerial surveillance context. Very good performance is achieved on challenging video sequences. Track fragmentation is nearly non-existent, and false alarm rates are low.