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A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking

机译：一种新方法以增强群体智能应用于视频目标跟踪

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This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to the problem of video tracking targets in environments with almost constant lighting. This approach also delimits the solution space for a more efficient search. A robust version to outliers of the double exponential smoothing (DES) model is used to predict the target position in the frame delimiting the solution space in a more promising region for target tracking. To assess the quality of the proposed approach, an appropriate tracker for a discrete solution space was implemented using the meta-heuristic Shuffled Frog Leaping Algorithm (SFLA) adapted to dynamic optimization problems, named the Dynamic Shuffled Frog Leaping Algorithm (DSFLA). The DSFLA was compared with other classic and current trackers whose algorithms are based on swarm intelligence. The trackers were compared in terms of the average processing time per frame and the area under curve of the success rate per Pascal metric. For the experiment, we used a random sample of videos obtained from the public Hanyang visual tracker benchmark. The experimental results suggest that the DSFLA has an efficient processing time and higher quality of tracking compared with the other competing trackers analyzed in this work. The success rate of the DSFLA tracker is about 7.2 to 76.6% higher on average when comparing the success rate of its competitors. The average processing time per frame is about at least 10% faster than competing trackers, except one that was about 26% faster than the DSFLA tracker. The results also show that the predictions of the robust DES model are quite accurate.
机译：这项工作提出了一种新方法来改善群体智能算法，通过促进知识转移与粒子的多样性之间的平衡。该提出的方法被设计为应用于几乎恒定照明的环境中的视频跟踪目标问题。此方法还界定了解决方案空间以获得更有效的搜索。双指数平滑（DES）模型的异常值的强大版本用于预测帧中的目标位置，将解决方案空间划定在更有希望的区域进行目标跟踪中。为了评估所提出的方法的质量，使用适用于动态优化问题的元启发式混合青蛙跨越算法（SFLA）来实现用于离散解决方案的适当跟踪器。将DSFLA与其他经典和当前跟踪器进行比较，其算法基于群智能。在每个框架的平均处理时间和每个Pascal指标的成功率下的区域的平均处理时间方面进行比较。对于实验，我们使用了从公共汉阳视觉跟踪基准测试获得的随机视频样本。实验结果表明，与在这项工作中分析的其他竞争跟踪器相比，DSFLA具有高效的处理时间和更高的跟踪质量。在比较其竞争对手的成功率时，DSFLA跟踪器的成功率在平均水平较高约7.2至76.6％。除了比DSFLA跟踪器快约26％之外，每个帧的平均处理时间速度速度快至少10％。结果还表明，鲁棒DES模型的预测是非常准确的。

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