Recently correlation filter based trackers have attracted considerable attention for their high computational efficiency. However, they cannot handle occlusion and scale variation well enough. This paper aims at preventing the tracker from failure in these two situations by integrating the depth information into a correlation filter based tracker. By using RGB-D data, we construct a depth context model to reveal the spatial correlation between the target and its surrounding regions. Furthermore, we adopt a region growing method to make our tracker robust to occlusion and scale variation. Additional optimizations such as a model updating scheme are applied to improve the performance for longer video sequences. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracker performs favourably against state-of-the-art algorithms.
OpenCL programming provides full code portability between different hardware platforms,and can serve as a good programming candidate for heterogeneous systems,which typically consist of a host processor and several accelerators.However,to make full use of the computing capacity of such a system,programmers are requested to manage diverse OpenCL-enabled devices explicitly,including distributing the workload between different devices and managing data transfer between multiple devices.All these tedious jobs pose a huge challenge for programmers.In this paper,a distributed shared OpenCL memory(DSOM) is presented,which relieves users of having to manage data transfer explicitly,by supporting shared buffers across devices.DSOM allocates shared buffers in the system memory and treats the on-device memory as a software managed virtual cache buffer.To support fine-grained shared buffer management,we designed a kernel parser in DSOM for buffer access range analysis.A basic modified,shared,invalid cache coherency is implemented for DSOM to maintain coherency for cache buffers.In addition,we propose a novel strategy to minimize communication cost between devices by launching each necessary data transfer as early as possible.This strategy enables overlap of data transfer with kernel execution.Our experimental results show that the applicability of our method for buffer access range analysis is good,and the efficiency of DSOM is high.
OpenCL is an open heterogeneous programming framework. Although OpenCL programs are func- tionally portable, they do not provide performance portability, so code transformation often plays an irreplaceable role. When adapting GPU-specific OpenCL kernels to run on multi-core/many-core CPUs, coarsening the thread granularity is necessary and thus has been extensively used. However, locality concerns exposed in GPU-specific OpenCL code are usually inherited without analysis, which may give side-effects on the CPU performance. Typi- cally, the use of OpenCL＇s local memory on multi-core/many-core CPUs may lead to an opposite performance effect, because local-memory arrays no longer match well with the hardware and the associated synchronizations are costly. To solve this dilemma, we actively analyze the memory access patterns using array-access descriptors derived from GPU-specific kernels, which can thus be adapted for CPUs by （1） removing all the unwanted local-memory arrays together with the obsolete barrier statements and （2） optimizing the coalesced kernel code with vectorization and locality re-exploitation. Moreover, we have developed an automated tool chain that makes this transformation of GPU-specific OpenCL kernels into a CPU-friendly form, which is accompanied with a scheduler that forms a new OpenCL runtime. Experiments show that the automated transformation can improve OpenCL kernel performance on a multi-core CPU by an average factor of 3.24. Satisfactory performance improvements axe also achieved on Intel＇s many-integrated-core coprocessor. The resultant performance on both architectures is better than or comparable with the corresponding OpenMP performance.
