# Parallelization of the Honeybee Search Algorithm for Object Tracking
## 4 Discussion  
The main concern of this work is to find out whether our proposed combination of HSA and GPU is capable of accelerating a given object tracker. It is shown in Figures 12 and 13 that the optimal proposal, the one that maximizes accuracy and speed, is parallel HSA. An area of opportunity that arises from this work is whether or not parallel HSA using NCC as fitness function can compete against other trackers in terms of speed. During the tests combining the GPU, HSA, and NCC, the minimum observed latency was of 0.0351 s per frame; the maximum was 2.6715 s per frame; and the average was of 0.3729 s per frame. The results show that the tracker's speed is affected by the dimensions of the frame (width and height); as well as the size of the tracked object.  
We reviewed the literature and found that the ALOV dataset was not used to test the speed of trackers due to the authors' emphasis on accuracy. On the other hand, the dataset presents a demanding task because the frame sizes that range from 192 \(\times\) 144 to 1920 \(\times\) 1080 pixels and post many different challenges. Other authors have reported speed of their tracker proposals. For example, [PERSON] et al. [4] which used video sequences with frame sizes than range from 128 \(\times\) 96 to 640 \(\times\) 480 pixels, which are significantly smaller than ALOV frame sizes. Using the data of the most accurate trackers reported in that benchmark [4], we found that the minimal latency is of 0.0027 s per frame, the maximum latency is of 1.9607 s per frame and the average is 0.3228 s per frame. In contrast our average time (0.3729 s per frame) seems to fall on the average, despite the fact that the ALOV dataset has larger frame sizes and presents more complex challenges.  
This work presents the results of tests using only one object tracker (NCC), but the parallelized version of the Honeybee Search Algorithm as presented could and ought to be combined with other object trackers. This combination can provide more evidence of the benefits of combining these tools. Testing with other object trackers is not a trivial undertaking since this requires the study and understanding of the tracker and an analysis to determine if it can be accurately translated into a fitness function. We believe the main objective of this work has been fulfilled by providing the proof that the combination of HSA and the GPU is useful to accelerate the NCC object tracker without adverse effects on its accuracy. For future work, we have decided to address this further by testing this configuration with other trackers (Section 5.1). Another relevant point that supports the previous observation is that the parallel HSA was capable of accelerating NCC to the point of reaching Struck and SiamMask speed levels even when NCC is not a state of the art tracker as is confirmed in Section 3.7.  
According to our results, the GPU may not be the best tool for the parallelization of HSA or other Swarm Intelligence algorithms. The GPU was designed to perform vector operations with small synchronization requirements, while many Swarm Intelligence algorithms focus on the idea of synchronization of individuals. Usually, a GPU has a limit to the number of possible synchronized threads, often a fraction of the full capacity of the GPU. Nevertheless, our parallel implementation of HSA achieved a higher speed than naive parallelization of the NCC tracker. Even though, the used hardware is not the best alternative in the market, we found that our parallelization approach enhances the performance of the NCC tracker and this should be translatable to another GPU with higher capacities.