Visual tracking based on semantic and similarity learning

We present a method by combining the similarity and semantic features of a target to improve tracking performance in video sequences. Trackers based on Siamese networks have achieved success in recent competitions and databases through learning similarity according to binary labels. Unfortunately, such weak labels result in limiting the discriminative ability of the learned feature, thus it is difficult to identify the target itself from the distractors that have the same class. The authors observe that the inter‐class semantic features benefit to increase the separation between the target and the background, even distractors. Therefore, they proposed a network architecture which uses both similarity and semantic branches to obtain more discriminative features for locating the target accuracy in new frames. The large‐scale ImageNet VID dataset is employed to train the network. Even in the presence of background clutter, visual distortion, and distractors, the proposed method still maintains following the target. They test their method with the open benchmarks OTB and UAV123. The results show that their combined approach significantly improves the tracking ability relative to trackers using similarity or semantic features alone

Learning Future-Aware Correlation Filters for Efficient UAV Tracking

In recent years, discriminative correlation filter (DCF)-based trackers have made considerable progress and drawn widespread attention in the unmanned aerial vehicle (UAV) tracking community. Most existing trackers collect historical information, e.g., training samples, previous filters, and response maps, to promote their discrimination and robustness. Under UAV-specific tracking challenges, e.g., fast motion and view change, variations of both the target and its environment in the new frame are unpredictable. Interfered by future unknown environments, trackers that trained with historical information may be confused by the new context, resulting in tracking failure. In this paper, we propose a novel future-aware correlation filter tracker, i.e., FACF. The proposed method aims at effectively utilizing context information in the new frame for better discriminative and robust abilities, which consists of two stages: future state awareness and future context awareness. In the former stage, an effective time series forecast method is employed to reason a coarse position of the target, which is the reference for obtaining a context patch in the new frame. In the latter stage, we firstly obtain the single context patch with an efficient target-aware method. Then, we train a filter with the future context information in order to perform robust tracking. Extensive experimental results obtained from three UAV benchmarks, i.e., UAV123_10fps, DTB70, and UAVTrack112, demonstrate the effectiveness and robustness of the proposed tracker. Our tracker has comparable performance with other state-of-the-art trackers while running at ∼49 FPS on a single CPU

Relationship between Adiponectin Gene Polymorphisms and Late-Onset Alzheimer's Disease.

In recent years, researchers have found that adiponectin (ANP) plays an important role in the pathogenesis of Alzheimer's disease (AD), and low serum concentrations of ANP are associated with AD. Higher plasma ANP level have a protective effect against the development of cognitive decline, suggesting that ANP may affect AD onset. Meanwhile, accumulating evidence supports the crucial role of ANP in the pathogenesis of AD. To study the relationship between ANP gene polymorphisms (rs266729, -11377C>G and rs1501299, G276T) and late-onset AD (LOAD), we carried out a case-control study that included 201 LOAD patients and 257 healthy control subjects. Statistically significant differences were detected in the genotype and allelotype frequency distributions of rs266729 and rs1501299 between the LOAD group and the control group, with a noticeable increase in the G and T allelotype frequency distributions in the LOAD group (P 0.05) between the LOAD group and control group, whereas the CG and GT haplotypes were significantly different (P < 0.05), suggesting a negative correlation between the CG haplotype and LOAD onset (OR = 0.74, 95% CI = 0.57-0.96, P = 0.022), and a positive correlation between the GT haplotype and LOAD onset (OR = 2.29, 95% CI = 1.42-3.68, P = 0.005). Therefore, we speculated that the rs266729 and rs1501299 of ANP gene polymorphisms and the GT and CG haplotypes were associated with LOAD

Spectrometric peak chart of rs266729.

<p>(A) <i>ANP</i> gene rs266729 (-11377C>G) CC genotype. (B) <i>ANP</i> gene rs266729 (-11377C>G) CG genotype. (C) <i>ANP</i> gene rs266729 (-11377C>G) GG genotype.</p

Spectrometric peak chart of rs1501299.

<p>(A) <i>ANP</i> gene rs1501299 (G276T) GG genotype. (B) <i>ANP</i> gene rs1501299 (G276T) GT genotype. (C) <i>ANP</i> gene rs1501299 (G276T) TT genotype.</p

Correlation analysis between <i>ANP</i> gene haplotypes and AD.

<p>* <i>P</i> < 0.05 indicates statistically significant differences.</p><p>Correlation analysis between <i>ANP</i> gene haplotypes and AD.</p

Comparison of clinical characteristics between the AD and control groups.

<p>* <i>P</i> < 0.05. ≤ primary school, illiterate or educated to primary school level;> primary school, educated above primary school level; BMI, body mass index;TC, total cholesterol; TG, triglyceride; LDL, low-density lipoprotein; HDL, high-density lipoprotein.</p><p>Comparison of clinical characteristics between the AD and control groups.</p