## 5 Implementation
### Recognition and Tracking Module
#### 5.3.4. Tracking
After 1:54 min of the video, 19 people get in and out of the scene and walk around, generating multiple occlusions among them and with objects of the scene. A good performance of the module is characterized by an accurate tracking, where data are processed and sent to the Pattern Detection module in real-time (i.e., in less than a second). Thus, the metrics used in the experimental analysis focus on the final result of the module and not in partial filter results. In order to evaluate the processing efficiency, the time required to process each frame was collected and used to calculate the average and maximum processing time per frame. The MOTChallenge benchmark, a unified evaluation platform created by [PERSON] et al. [40], is used to evaluate the tracking accuracy. Experiments were executed on Xeon Gold 6138 processors with 128 GB of RAM, from the National Supercomputing Center (Cluster-UY), Uruguay [41]. The MOTChallenge benchmark consists of three components: (i) a public dataset including its own and well known videos (some of them including ground truth information, like PETS09-S2L1, which is used for the evaluation of the proposed system); (ii) a centralized evaluation method that allows the comparison of results obtained with different methods; and (iii) an infrastructure that makes the crowdsourcing of new data possible, new evaluation methods, and new notations (i.e., ground truth). The evaluation method included in MOTChallenge provides several metrics. One of them, Multiple Object Tracking Accuracy (MOTA) was applied to evaluate the tracking accuracy of the proposed system. MOTA is a percentage that combines three indicators: false positives, false negatives, and identity changes of the tracked persons. The greater the MOTA value is, the more accurate is the tracking of the persons. Furthermore, the average and maximum difference between the number of persons in each frame (from the ground truth) and the detected tracklets and blobs in each frame are evaluated. The system has a set of parameters that determine how accurate the module performs in a given scene. Thus, previously to the evaluation, a set of experiments was performed to find the best parameter configuration. Since the module has a pipes and filters architecture, the performance of each filter depends only on its configuration values, so finding the best configuration values for each filter results in the best configuration for the entire module. Taking into account these considerations, different values for 40 parameters were studied. A total number of 1458 experiments were performed. For each executed block, three configurations were selected to process the next block. To find the best performing configuration, the following three criteria were taken into account: (i) higher MOTA value (M-MOTA), (ii) lower average difference in the person counting (M-Count), and (iii) from the ones with higher MOTA value, the one with lower average processing time per frame (M-MOTA-TC). Full details of configuration experiments and best values are reported in the complete execution plan available in the project website fing.edu.uy/inco/grupos/eccal/hpc/APMP. The baseline for the comparison was the manual configuration obtained by empirical tuning and used during the development of the system. The three configurations that achieved the best results in the configuration experiments used \(k\)-NN and were able to improve the MOTA value up to 14.8% and the person counting up to 34%. In addition, the highest MOTA value obtained by the system (52.7) was higher than the average MOTA value (36.6) obtained by algorithms in the 2D MOT 2015 benchmark [42]. However, it is worth noting that results for the proposed system correspond to anevaluation performed on a single scene, while results for the 2D MOT 2015 benchmark correspond to averages obtained from applying different algorithms to multiple scenes.