Object-tracking algorithms on embedded platforms are very important in many civilian and military applications. The Tracking–Learning–Detection (TLD) algorithm is considered one of the state-of-the-art online long-term object-tracking algorithms. The performance of running such computationally intensive algorithms on embedded platforms with limited computing resources is a challenge. This work proposes an enhanced TLD implementation, specifically designed for, and tested on, embedded platforms. In this new implementation, an extra-stage has been added to the TLD detector cascade, called a Region filter. This filter dynamically identifies the candidate region for the tracked object. Further, the two independent tracker and detector TLD components, and the two independent Forward–Backward (FB) and Normalized Cross Correlation (NCC) error measures in the tracker have been parallelized. Still further, the computations of Image Integral in the detector and the NCC in both the tracker and the detector have been optimized using a single instruction multiple data (SIMD) architecture. We evaluate our proposed implementation on the Apalis T30 embedded platform, using the same video sequences that the original TLD is evaluated on. Our results show that our enhanced implementation outperforms the baseline with an average speedup of 3.7 × in the total number of frames per second (fps), while achieving an average 91% of the Precision and 86.6% of the Recall metrics, across all sequences. Further, our enhanced implementation achieves an average speedup of 4.52 × and 1.86 × in the detector and tracker execution times, respectively. Moreover, it results in an average 66.3% energy saving, as compared to the original implementation.

在许多民用和军事应用中，嵌入式平台上的对象跟踪算法非常重要。跟踪-学习-检测（TLD）算法被认为是最新的在线长期对象跟踪算法。在具有有限计算资源的嵌入式平台上运行这种计算密集型算法的性能是一个挑战。这项工作提出了一种增强的TLD实现，专门针对嵌入式平台设计并经过测试。在这种新的实现方式中，已将额外级添加到TLD检测器级联中，称为区域过滤器。该过滤器动态识别跟踪对象的候选区域。此外，跟踪器中的两个独立的跟踪器和检测器TLD组件，以及两个独立的向前-向后（FB）和归一化互相关（NCC）误差度量已经并行化。更进一步，已经使用单指令多数据（SIMD）架构优化了检测器中图像积分的计算以及跟踪器和检测器中NCC的计算。我们使用与评估原始TLD相同的视频序列，评估在Apalis T30嵌入式平台上提出的实施方案。我们的结果表明，我们增强的实现优于基线，平均每秒总帧数为3.7×（fps），同时在所有序列中平均获得91％的Precision和86.6％的Recall指标。此外，我们的增强实现在检测器和跟踪器的执行时间上分别实现了4.52×和1.86×的平均加速。此外，与原始实施相比，它平均节省了66.3％的能源。