Abstmct-In this correspondence, we show how to recover the motion of an observer relative to a planar surface from image brightness derivatives. We do not compute the optical flow as an intermediate step, only the spatial and temporal brightness gradients (at a minimum of eight points). We first present two iterative schemes for solving nine nonlinear equations in terms of the motion and surface parameters that are derived from a least-squares fomulation. An initial pass over the relevant image region is wed to accumulate a number of moments of the image brightness derivatives. All of the quantities used in the iteration are efficiently computed from these totals without the need to refer back to the image. We then show that either of two possible solutions can be obtained in closed form. We first solve a linear matrix equation for the elements of a 3 x 3 matrix. The eigenvalue decomposition of the symmetric part of the matrix is then used to compute the motion parameters and the plane orientation. A new compact notation allows us to show easily that there are at most two planar solutions.

IEEE Transactions on Pattern Analysis and Machine Intelligence

The invention discloses a tracking-learning-detection (TLD)-based video object tracking method, which comprises the following steps of: (1) when a start frame of an object is tracked, generating an image sub-window according to given position and size information, and training a detector; (2) estimating the area of the object in a current frame according to the position and size information of the object in a previous frame by using a tracker; (3) detecting the current frame by using the detector, and finding all possible objects in the current frame; and (4) performing fusion processing on results of the tracker and the detector, judging whether the object exists in the current frame or not, returning to the step (3) to process the next frame if the object does not exist in the current frame, judging whether a tracking trajectory of the current frame is effective or not if the object exists in the current frame, giving the position of the object, returning to the step (2) to process the next frame if the tracking trajectory of the current frame is ineffective, finishing the online updating of the detector if the tracking trajectory of the current frame is effective, and returning to the step (2) to process the next frame. According to the method, the conventional TLD algorithm is improved to obtain a video object tracking algorithm which is more ideal than the TLD algorithm.

Tracking-learning-detection (TLD)-based video object tracking method

Hyperspectral (HS) video is able to capture abundant spectral, spatial, and temporal information about objects, which overcomes the limitations of common red-green-blue (RGB) video in complex scenarios such as similar appearances and background clutters (BCs). However, most trackers apply hand-crafted features extracted from manually selected bands instead of deep features for object representations due to limited HS data and the band gap problem. Each HS image consists of many bands, and it is challenging to fully interact with the band information while maintaining tracking speed. To this end, this article proposes a novel end-to-end spectral awareness interaction network with a dynamic template (SPIRIT) for HS video object tracking. First, a spectral awareness module (SAM) is proposed to learn band contributions with consideration of nonlinear and global interactions between HS bands. It can also cooperate with the feature extraction module pretrained with RGB data to attenuate the band gap and data-hungry. Second, an interaction module (IM) is proposed to achieve inter and intraband feature interactions to enhance tracking performance while improving efficiency. Furthermore, the proposed method contains a novel update module (UM) that evaluates the tracking confidence of the current state to adapt to object changes and attenuate tracking drifts. Extensive experiments demonstrate the superiority of our approach compared to state-of-the-arts (SOTAs) while meeting real-time demands.

SPIRIT: Spectral Awareness Interaction Network With Dynamic Template for Hyperspectral Object Tracking

Visual object tracking is a fundamental task in computer vision and thrived in recent decades. With the development of snapshot hyperspectral (HS) sensors, efforts have been made to exploit tracking the object with HS videos to overcome the inherent limitation of red–green–blue (RGB) images. Existing HS tracking algorithms extract the deep features from image data separately, which break the interaction information between bands. Therefore, the discrimination ability of HS trackers is limited and the efficiency of the existing HS algorithms is low. In this article, a novel algorithm [cross-band feature fusion network (CBFF-Net)] is proposed for HS object tracking to improve the discrimination ability and reduce the computational complexity. Specifically, the backbone and head network are implemented with modules of a transferred RGB object tracking network to carry out the HS target tracking task while maintaining the discrimination ability learned from RGB data. Moreover, a bidirectional multiple deep feature fusion (BMDFF) module is proposed to fuse the features extracted from different bands of the HS images, and a cross-band group attention (CBGA) module is introduced to learn interaction information across bands of the HS images. Experiments results indicate the superiority in performance of CBFF-Net, and it runs at 24 frames/s (FPS).

CBFF-Net: A New Framework for Efficient and Accurate Hyperspectral Object Tracking

Hyperspectral videos (HSVs) have more potential in object tracking than color videos, thanks to their material identification ability. Nevertheless, previous works have not fully explored the benefits of the material information, resulting in limited representation ability and tracking accuracy. To address this issue, this article introduces a material-guided multiview fusion network (MMF-Net) for improved tracking. Specifically, we combine false-color information, hyperspectral information, and material information obtained by hyperspectral unmixing to provide a rich multiview representation of the object. Cross-material attention (CMA) is employed to capture the interaction among materials, enabling the network to focus on the most relevant materials for the target. Furthermore, leveraging the discriminative ability of material view, a novel material-guided multiview fusion module is proposed to capture both intraview and cross-view long-range spatial dependencies for effective feature aggregation. Thanks to the enhanced representation ability of each view and the integration of the complementary advantages of all views, our network is more capable of suppressing the tracking drift in various challenging scenes and achieving accurate object localization. Extensive experiments show that our tracker achieves state-of-the-art tracking performance. The source code will be available at https://github.com/hscv/MMF-Net.

Material-Guided Multi-View Fusion Network for Hyperspectral Object Tracking

Object detection has always been a hot topic in image processing, which is important in a variety of applications. With the advent of the era of big data and the continuous improvement of hardware computing power, deep learning gets more attention in object detection. One popular branch is regression-based (One-stage) model, which uses a single neural network to directly predict bounding boxes and class probabilities from the entire image by one evaluation. One-stage networks can effectively increase the detection speed. This article mainly describes object detection methods based on regression object detectors (One-stage methods), such as You Only Look Once (YOLO) series and Single Shot Multibox Detector (SSD) series. Then, their applications are briefly introduced. The development trend and future development direction of this type of object detection are discussed in the end.

A Comprehensive Review of One-stage Networks for Object Detection

Hyperspectral object tracking aims to take advantage of the rich spatial and spectral information in hyperspectral videos to effectively improve the robustness and accuracy of object tracking. Compared with color videos, hyperspectral videos have huge amount of data bringing a challenge to the efficiency of object tracking. We propose a fast hyperspectral object tracking method based on channel selection strategy. The strategy considers the spatial and spectral changes of local regions in the frame image and selects only three channels fed to tracker to speed up. The experimental results show that our method reaches 11.5 FPS on the dataset of Hyperspectral Object Tracking Challenge, which is faster than the state-of-the-art methods.

A Fast Hyperspectral Object Tracking Method Based On Channel Selection Strategy

Tracking with kernelized correlation filters (KCF) is an excellent object tracking algorithm, which is widely concerned. In KCF, each candidate patch in tracked region corresponds to a confidence ratio indicating the probability of containing the target, and the patch with the maximum confidence ratio is the output. In traditional KCF, its tracking performance decreases in complex scenes and the model is liable to be contaminated due to updated every frame. To overcome these limitations, we combine all available confidence ratios to form a confidence map, then by analyzing the confidence map, we infer the tracking scene and adopt different tracking strategies. For complex scenes, we dynamically improve KCF to enhance its tracking performance. In addition, we propose an innovative model update mechanism to reduce the computational complexity and model contamination. The experimental results show that compared with the conventional KCF algorithm, the proposed approach improves success rate and precision by 7% and 8% respectively.

Confidence map based KCF object tracking algorithm

Super-resolution image restoration produces a high-resolution image from a set of shifted, blurred, and decimated versions thereof. The quality of super-resolution depends on the accuracy and the amount of a priori information that could be utilized in the processing. In this paper, a POCS-embedded MAP based super-resolution image restoration algorithm is proposed, which incorporates POCS constraints and MAP algorithm. Experiments demonstrate that POCS-embedded MAP algorithm can achieve a better restoration result than traditional MAP and POCS approach.

POCS-embedded MAP method for image super-resolution restoration

With the rapid development of hyperspectral imaging technology, object tracking in hyperspectral video has become a research hotspot. Real-time object tracking for hyperspectral video is a great challenge. We propose a fast hyperspectral object tracking method via a channel selection strategy to improve the tracking speed significantly. First, we design a strategy of channel selection to select few candidate channels from many hyperspectral video channels, and then send the candidates to the subsequent background-aware correlation filter (BACF) tracking framework. In addition, we consider the importance of local and global spectral information in feature extraction, and further improve the BACF tracker to ensure high tracking accuracy. In the experiments carried out in this study, the proposed method was verified and the best performance was achieved on the publicly available hyperspectral dataset of the WHISPERS Hyperspectral Objecting Tracking Challenge. Our method was superior to state-of-the-art RGB-based and hyperspectral trackers, in terms of both the area under the curve (AUC) and DP@20pixels. The tracking speed of our method reached 21.9 FPS, which is much faster than that of the current most advanced hyperspectral trackers.

https://www.mdpi.com/2072-4292/15/6/1557/pdf?version=1678779147

A Fast Hyperspectral Tracking Method via Channel Selection

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