GlobalTrack: A Simple and Strong Baseline for Long-term Tracking

A key capability of a long-term tracker is to search for targets in very large areas (typically the entire image) to handle possible target absences or tracking failures. However, currently there is a lack of such a strong baseline for global instance search. In this work, we aim to bridge this gap. Specifically, we propose GlobalTrack, a pure global instance search based tracker that makes no assumption on the temporal consistency of the target's positions and scales. GlobalTrack is developed based on two-stage object detectors, and it is able to perform full-image and multi-scale search of arbitrary instances with only a single query as the guide. We further propose a cross-query loss to improve the robustness of our approach against distractors. With no online learning, no punishment on position or scale changes, no scale smoothing and no trajectory refinement, our pure global instance search based tracker achieves comparable, sometimes much better performance on four large-scale tracking benchmarks (i.e., 52.1% AUC on LaSOT, 63.8% success rate on TLP, 60.3% MaxGM on OxUvA and 75.4% normalized precision on TrackingNet), compared to state-of-the-art approaches that typically require complex post-processing. More importantly, our tracker runs without cumulative errors, i.e., any type of temporary tracking failures will not affect its performance on future frames, making it ideal for long-term tracking. We hope this work will be a strong baseline for long-term tracking and will stimulate future works in this area. Code is available at https://github.com/huanglianghua/GlobalTrack.Comment: Accepted in AAAI202

Visual Tracking by Sampling in Part Space

In this paper, we present a novel part-based visual tracking method from the perspective of probability sampling. Specifically, we represent the target by a part space with two online learned probabilities to capture the structure of the target. The proposal distribution memorizes the historical performance of different parts, and it is used for the first round of part selection. The acceptance probability validates the specific tracking stability of each part in a frame, and it determines whether to accept its vote or to reject it. By doing this, we transform the complex online part selection problem into a probability learning one, which is easier to tackle. The observation model of each part is constructed by an improved supervised descent method and is learned in an incremental manner. Experimental results on two benchmarks demonstrate the competitive performance of our tracker against state-of-the-art methods

Discriminative tracking using tensor pooling

How to effectively organize local descriptors to build a global representation has a critical impact on the performance of vision tasks. Recently, local sparse representation has been successfully applied to visual tracking, owing to its discriminative nature and robustness against local noise and partial occlusions. Local sparse codes computed with a template actually form a three-order tensor according to their original layout, although most existing pooling operators convert the codes to a vector by concatenating or computing statistics on them. We argue that, compared to pooling vectors, the tensor form could deliver more intrinsic structural information for the target appearance, and can also avoid high dimensionality learning problems suffered in concatenation-based pooling methods. Therefore, in this paper, we propose to represent target templates and candidates directly with sparse coding tensors, and build the appearance model by incrementally learning on these tensors. We propose a discriminative framework to further improve robustness of our method against drifting and environmental noise. Experiments on a recent comprehensive benchmark indicate that our method performs better than state-of-the-art trackers

Prognostic significance of circulating tumor cell measurement in the peripheral blood of patients with nasopharyngeal carcinoma

Objective: Nasopharyngeal Carcinoma (NPC) is lethal cancer. Typically, relapse and metastasis are the outcomes of most patients. Against this backdrop, this study aimed to investigate the correlation between Circulating Tumor Cell (CTC) profiles and clinicopathological features in patients with NPC. Patients and methods: A total of 119 blood samples from 79 patients were collected from patients with NPC during treatment. CanPatrolTM CTC enrichment and RNA In Situ Hybridization (RNA-ISH) were used to characterize CTCs, including epithelial, Mesenchymal (MCTCs), and epithelial/mesenchymal mixed types according to their surface markers. Results: The number of CTCs and MCTCs in the pre-treatment group was significantly higher than that in the post-treatment group (p < 0.05). The total number of CTCs and MCTCs cell numbers was significant correlation with Tumor-Node-Metastasis (TNM) staging (p < 0.05), Progression-Free Survival (PFS), and Overall Survival (OS). The PFS of patients with > 7 CTCs or > 5 MCTCs per 5 mL blood was significantly shorter PFS than those patients with ≤ 7 CTCs or ≤ 5 MCTCs (p < 0.05). Patients treated with targeted therapy combined with chemoradiotherapy had poorer PFS and OS rates than those treated with chemoradiotherapy (p < 0.05). The Kaplan-Meier survival analysis also demonstrated that patients with changes in CTC > 4 were strongly associated with PFS and OS rates (p < 0.05). Conclusion: CTC and MCTC number detection in patients with NPC is a useful biomarker for predicting patient progress. Patients with more than 7 CTCs or 5 MCTCs in 5 mL of blood had shorter PFS and OS rates. CTC and MCTC count changes were also significantly associated with the patient's therapy

Visual tracking under motion blur

Most existing tracking algorithms do not explicitly consider the motion blur contained in video sequences, which degrades their performance in real-world applications where motion blur often occurs. In this paper, we propose to solve the motion blur problem in visual tracking in a unified framework. Specifically, a joint blur state estimation and multi-task reverse sparse learning framework are presented, where the closed-form solution of blur kernel and sparse code matrix is obtained simultaneously. The reverse process considers the blurry candidates as dictionary elements, and sparsely represents blurred templates with the candidates. By utilizing the information contained in the sparse code matrix, an efficient likelihood model is further developed, which quickly excludes irrelevant candidates and narrows the particle scale down. Experimental results on the challenging benchmarks show that our method performs well against the state-of-the-art trackers

The first complete mitochondrial genome of Actinopyga from Actinopyga echinites (Aspidochirotida: Holothuriidae)

The deep-water redfish, Actinopyga echinites, is an ecologically and economically important holothuroid in China due to its valuable nutrition and pharmacological compounds. However, the taxonomy and phylogeny of the Actinopyga have been debated and misidentifications have been reported recently. Moreover, there remain considerable doubts about cryptic species complex within Actinopyga. In this study, we report the first complete mitochondrial genome of Actinopyga from A. echinites. The mitogenome has 15,619 base pairs (62.9% A + T content) and made up of a total of 37 genes (13 protein-coding, 22 transfer RNAs, and 2 ribosomal RNAs), and a putative control region. This study was the first available complete mitogenome of Actinopyga and will provide useful genetic information for future phylogenetic and taxonomic classification of Holothuriidae

The first complete mitochondrial genome of Bursidae from Bufonaria rana (Caenogastropoda: Tonnoidea)

The common frogsnail Bufonaria rana, is an ecologically and economically important Tonnoideans in China due to valuable nutrition and pharmacological compounds. However, the taxonomy and phylogeny of the Bursidae have been debated and synonyms among Bursidae species have been reported recently. In this study, we report the first complete mitochondrial genome of Bursidae from B. rana. The mitogenome has 15,510 base pairs (69.0% A + T content) and made up of total of 37 genes (13 protein-coding, 22 transfer RNAs and 2 ribosomal RNAs), and a putative control region. This study was the first available complete mitogenomes of Bursidae and will provide useful genetic information for future phylogenetic and taxonomic classification of Tonnoideans

The complete mitochondrial genome of marine gastropod Melo melo (neogastropoda: volutoidea)

Melo melo is an ecologically and economically important species of Neogastropoda, which is an ecologically diverse group of carnivorous marine gastropods. However, the taxonomic classification and phylogenetic studies have so far been limited. In this study, we report the second complete mitochondrial genome of Volutidae from M. melo. The mitogenome has 15,721 base pairs (68.3% A + T content) and made up of total of 37 genes (13 protein-coding, 22 transfer RNAs and 2 ribosomal RNAs), and a control region. This study was the second available complete mitogenomes of Volutidae and will provide useful genetic information for future phylogenetic and taxonomic classification of Neogastropoda

The first complete mitochondrial genome of Siphonosoma from Siphonosoma cumanense (Sipuncula, Sipunculidae)

Siphonosoma cumanense is economic important species in the fishery of southeast China. However, the current classification and the phylogeny of genus Siphonosoma had not been verified yet. Here, we report the complete mitochondrial genome sequence of S. Siphonosoma. The mitogenome has 15,917 base pairs and made up of total of 38 genes (13 protein-coding, 23 transfer RNAs and 2 ribosomal RNAs), and a putative control region. This study was the first available complete mitogenomes of Siphonosoma and will provide useful genetic information for future phylogenetic and taxonomic classification of Sipuncula