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Uniform silica dispersion is vital for achieving high-performance and low-rolling-resistance tires. Though silane coupling agents could improve the dispersion of silica, it would also lead to increasing processing complexity and volatile organic compounds. In the present work, amino-functionalized solution polymerized styrene–butadiene rubber (F-SSBR) were designed and synthesized aiming to form a hydrogen bond between the amino group and hydroxyl group in silica to improve the silica dispersion. In order to study the different effect of F-SSBR with in-chain or end-chain amino groups on silica dispersion, two 1,1-diphenylethylene (DPE) derivatives were introduced into the SSBR molecular chains via living anionic polymerization. Afterward, silica nanoparticles reinforced F-SSBR composites for tire tread were prepared. The effects of the functional groups of SSBR on the microstructure and performance of the composites were investigated. The results revealed that the incorporation of DPE derivatives into the SSBR molecular chain effectively decreased the size of silica aggregates and improved the dispersion of silica in the SSBR/silica composites. The thickness of tight bound rubber in F-SSBR/silica composite was larger than that of the original SSBR/silica composite. The F-SSBR composites had higher tensile strength, tear strength, and lower rolling resistance compared with the original SSBR composite. Especially, the F-SSBR with end-chain amino groups exhibited better comprehensive properties and had potential application in manufacturing high-performance tires.

The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term singleobject visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2015 is the first benchmark on shortterm tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Linköping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015.

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