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Visual Object Tracking on Multi-modal RGB-D Videos: A Review

The development of visual object tracking has continued for decades. Recent years, as the wide accessibility of the low-cost RGBD sensors, the task of visual object tracking on RGB-D videos has drawn much attention. Compared to conventional RGB-only tracking, the RGB-D videos can provide more information that facilitates objecting tracking in some complicated scenarios. The goal of this review is to summarize the relative knowledge of the research filed of RGB-D tracking. To be specific, we will generalize the related RGB-D tracking benchmarking datasets as well as the corresponding performance measurements. Besides, the existing RGB-D tracking methods are summarized in the paper. Moreover, we discuss the possible future direction in the field of RGB-D tracking.


I. INTRODUCTION
V Isual object tracking, as a fundamental research topic in computer vision and pattern recognition, aims to automatically predict the state of an object of interest in each frame of a video. It plays a significant role in various practical applications, including automatic driving, humancomputer interaction, intelligent surveillance, and so on [1], [2], [3]. Over the past decades, visual object tracking has achieved significant progress [4], [5], [6], [7]. Conventional visual object tracking mainly focus on RGB videos captured by a monocular camera. Despite the fact that considerable development has been achieved in the research of visual object tracking, due to some challenging factors in RGB videos such as background clutter, illumination variation, deformation, occlusion, etc, it is still a challenging task to implement robust tracking.
Trying to alleviate the limitation of RGB only devices, various reliable and low-priced RGB-D sensors have been widely accessible recent years. An RGB-D image is comprised of a three-channel RGB image and a single-channel depth map. On account of the supplementary spatial information provided by an accurate depth map, which can offset the defect of insufficient information of RGB image in some complicated scenes, RGB-D data has been widely utilized for multifarious computer vision applications [8], [9], [10], [11], [12]. In the visual object tracking community, the progress of RGB-D tracking research has been promoted significantly X since the emergence of several RGB-D tracking benchmarking datasets [13], [14], [15], [16]. Up to now, there are four RGB-D tracking benchmarking datasets in total including PTB [13], STC [14], CDTB [15] and DepthTrack [16]. The videos of these datasets are captured by different kinds of cameras including ToF-RGB pair of cameras, stereo-camera sensor and Kinect sensor. On these datasets, various measurements to evaluate the performance of RGB-D trackers are also proposed.
In general, the aim of RGB-D tracking is to track an object of interest with a given initial state in multi-modal RGB-D videos. Compared to the RGB-only tracking, an additional depth map can provide more 3D spatial perception, which can be adopted to avoid or address tracking failures caused by model drifts or severe occlusions [17], [18]. Considering this advantage that the depth map can benefit to address target occlusion and re-detect the target, as well as the excellent performance of current RGB-only tracker, the existing RGB-D trackers are generally based on high-performance short-term RGB trackers, using the depth information additionally for occlusion estimation [19]. Besides the challenge of occlusion, the problem of target disappearance and reappearance should also be more manageable in RGB-D tracking datasets. Accordingly, existing RGB-D tracking methods are endowed with a long-term tracking mechanism with a re-detection module to address the problem of tracking failures [15], [20], [21], [22]. Exploiting the depth channel with the help of a longterm tracking mechanism, RGB-D trackers have been shown to report a great performance improvement [20], [21].
To summarise, the key contributions of this work include the following: • We provide the details of the existing RGB-D tracking datasets and analyse the characteristic of each dataset. As well, the corresponding performance measurements are presented and analysed.
• We summarize the existing RGB-D tracking algorithms and give the corresponding analysis.
• We present the potential development trend of RGB-D tracking. We hope that it could provide some inspiration for relevant researchers.
The rest of this paper is organised as follows. In Section II, we discuss the existing datasets and corresponding performance measurement. In this part, we also present some comparison of the results of the existing RGB-D tracking. In Section III, we review the existing RGB-D tracking methods. In Section IV, we discuss the potential development trend of RGB-D tracking. Conclusions are offered in Section V. In the recent years, the rapid development of sensor equipment and the emergence of various RGB-D benchmarking datasets have significantly promoted the progress of the RGB-D tracking research. In this section, we mainly introduce the existing RGB-D datasets and corresponding performance measurements. According to different imaging principles, there are various kinds of RGB-D sensors, such as ToF-RGB pair of cameras, stereo-camera sensor and Kinect sensor and so on. All these camera can capture a three-channel RGB image and a single-channel depth map simultaneously. Four RGB-D tracking datasets, PTB [13], STC [14], CDTB [15] and DepthTrack [16] have been proposed. The sizes of these four datasets are provided in Table I.
Thanks to the emergence of the seminal publicly available RGB-D tracking dataset Princeton Tracking Benchmark (PTB) [13], the research on RGB-D tracking has drawn widespread attention. PTB contains 100 challenging videos for RGB-D tracking evaluation. Among them, 95 videos only give the groundtruth bounding boxes of the initial frame, and the remaining 5 videos provide all the groundtruth labels for the parameters tuning. According to the target category, target size, movement, occlusion and motion type, these sequences are annotated with 11 attributes, including human, animal, rigid, large, small, slow, fast, occlusion, no occlusion, passive and active. For the performance evaluation on PTB, Successful Rate (SR) is proposed. The SR is the percentage of frames where the overlap ratio between the predicted target bounding box and the ground truth annotation exceeds the threshold of 0.5. For evaluation, users can test their trackers on the 95 videos and submit the raw results to the online evaluation page to get the overall performance. For more details of the PTB dataset and the corresponding evaluation metric, the readers can refer to [13].
STC [14] is an RGB-D tracking dataset comprising 36 video sequences with 10 per-frame annotated attributes, including illumination variation (IV), depth variation (DV), scale variation (SV), colour distribution variation (CDV), depth distribution variation (DDV), surrounding depth clutter (SDC), surrounding colour clutter (SCC), background-colour camouflages (BCC), background shape camouflages (BSC) and partial occlusion (PO). To evaluate the performance of trackers, the Area Under Curve (AUC) as a measurement is used. The AUC is defined as the area under the curve of the success plot. The success plot shows the percentage of the instances where the bounding box overlap ratio exceeds a given threshold. The reference [14] provides more detailed information about the STC dataset and the corresponding evaluation metrics.
CDTB is the existing largest testing dataset for RGB-D tracking, comprising 80 video sequences captured in a longterm tracking scenario. All these sequences are annotated perframe with thirteen attributes, including fast motion, target size change, aspect ratio change, similar targets, target out-of-view, full occlusion, partial occlusion, out-of-plane rotation, similar objects, deformable target, reflective target, depth change and dark scene. Considering that during a long term tracking, the target may disappear and reappear several times, the ability to localise the target as well as to predict the target absence, and re-detect the missed target, is very important. In this case, as to tracking performance evaluation on longterm scenarios, the high-profile VOT long-term tracking challenges [23], [20], [21], [22] have proposed long-term tracking evaluation protocol measure the tracking performance on the CDTB dataset. The tracking Precision and Recall are applied as the performance measures. Specifically, the Precision is defined as the average ratio of overlap of the predicted and ground truth targets on the frames where the target is detected. The Recall represents the average ratio of overlap of the predicted target bounding box and the ground truth annotation, measured on the frames where the target is visible. The primary performance measurement on the CDTB dataset is F-score obtained by calculating the tracking F-measurement that combines tracking Precision and Recall. More specific information about the CDTB dataset and the performance measurements is provided in [15].
DepthTrack [16] is the existing largest RGB-D tracking dataset, and is also the first dataset for offline training of RGB-D trackers. It contains 200 RGB-D video sequences captured by using the Intel Realsense 415 sensor, in which 150 videos can be adopted for the training offline of end-to-end network based RGB-D tracker. The remaining 50 videos are used for the tracking performance evaluation. For detailed scene-type level analysis of tracking results, all videos are annotated 15 per-frame attributes, in which 13 attributes are the same as the attributes of CDTB. Two additional attributes, background clutter and camera motion, are introduced in DepthTrack. Similar to CDTB, DepthTrack is a long-term RGB-D tracking dataset, therefore, the performance metrics of the DepthTrack are the same as the metrics of CDTB. More details about the DepthTrack can be available in [16].     [33], fusing colour and depth cues as features, with the depth information used to manage occlusions and scale changes [31]. Similarly, exploiting the depth cue to handle occlusions, scale variation, and shape change, a real-time RGB-D tracker DS-KCF, built upon the fast KCF tracker, is proposed [30]. Awwad et al. propose a local depth pattern feature for RGB-D tracking based on the colour-only Struck tracker [34]. To convert an arbitrary short-term RGB tracker into an RGB-D tracker, Kart et al. propose a general framework [19] and adopt a novel depth segmentation-based occlusion detection module into the discriminative correlation filter framework [29]. More recently, a long-term RGB-D tracker OTR based on a DCF tracker is proposed to model appearance changes via 3D target reconstruction [25]. Based on ECO tracker [35], Jiang et al. propose hierarchical multimodal fusion to fuse the features from RGB image and depth map [36]. Secondly, there are also some RGB-D trackers based on other RGB-only trackers, such as conventional mean-shift trackers and current end-to-end offline trained RGB trackers. Not limiting the use of the depth information only for occlusion reasoning, Xiao et al. propose an adaptive range-invariant target depth model to exploit depth in tracking [14]. Considering that 3-D point clouds can be generated from RGB-D data, a 3-D extension of the classical mean-shift tracker is proposed to deal with occlusions more effectively [26]. Recently, as a result of the incorporation of a long-term RGB-D tracking benchmark with a clearly defined performance evaluation methodology in the VOT toolkit [15], more and more advanced algorithms based on end-to-end offline trained RGB trackers are proposed for the VOT-RGBD challenge, focusing on different ways of exploring depth information and designing effective long-term tracking mechanisms [20], [21], [22].

III. RGBD TRACKING METHODS
Thirdly, Yan et al. proposes a end-to-end offline trained RGB-D tracker DeT [16] based on the framework of the RGBonly trackers ATOM [37] and DiMP [38], using a additional feature fusion module. As far as we know, it is the first offline trained RGB-D tracking method. Considering the fact that the offline trained algorithms require the support of large-scale data, they generate a considerable number of RGB-D videos by conducting monocular depth prediction on existing RGB tracking training data. With a small amount of real RGB-D videos of the training set of DepthTrack and a large number of generated RGB-D videos, the offline trained RGB-D tracker DeT achieves remarkable performance on the testing set of DepthTrack.
For the articles and codes of the above methods, readers can refer to the relevant references. Here we also collect some experimental results of existing RGB-D methods for the convenience of readers, as shown in Table II, III, IV, V. IV. FUTURE DIRECTIONS Although the RGB-D tracking has achieved significant progress recent yeas, there is still a huge space for improvement. Most of the existing RGB-D trackers fail to exploit all the visual information conveyed by the two modalities efficiently, only using the depth channel for occlusion reasoning and target re-detection. For comptuter vison task, robustness and expressiveness of features are critical to methods performance [39], [40], [41], [42], [43]. For RGB-D data, the modal asymmetry of RGB and depth is obvious, therefore how to extract robust features from RGB and depth modalities is extremely important to improve the performance of tracking. Additionally, the fusion of the features of two modality is also vital for multi-modal tracking, due to the fact that information fusion is beneficial to research of multi-modal computer vision tasks [44], [45], [46], [47], [48]. Besides, in view of the scarcity of RGB-D tracking videos for offline training of an RGB-D tracking network, self-supervised learning method can be considered. Moreover, most existing RGB-D tracking algorithms are learned online rather than trained offline with sufficient RGB-D data. Considering the outstanding performance of current RGB-only trackers offline trained using large-scale RGB videos, existing RGB-D tracking data needs to be augmented. Futhermore, the RGB-D image can be converted to 3D point cloud data, so that some processing methods for 3D point cloud data can be applied to RGB-D tracking. With the increase of RGB-D annotation data and the proposal of various algorithms in the future, the RGB-D tracking filed will witness a great development.

V. CONCLUSION
In this paper, we present a brief review of multi-modal RGB-D tracking. Firstly, We provide the details of the existing RGB-D tracking datasets and analyse the characteristic of each dataset. As well, the corresponding performance measurements are presented and analysed. Then, we summarize the existing RGB-D tracking algorithms and collect the results of the existing RGB-D trackers. Finally, we present the potential directions of RGB-D tracking. We hope this brief review can give valuable insight into the research of RGB-D tracking and encourage new research.