# Novel Hand Gesture Alert System
## 3 Proposed Model
### Human Detector and Regions of Interest (ROIs)  
Researchers are addressing the problem of multiple object tracking (MOT) with neural networks. They are doing this primarily by building robust models that capture information about movement, appearance, and interactions between objects. Considering the issue of MOT, we adopt a conventional methodology to follow different hypotheses with Kalman recursive filtering and image-by-image association. To further illustrate our idea, consider the following situation: when an object is obstructed for a more extended period, the following Kalman filter predictions increase the uncertainty associated with the position of the object (see Figure 4). We use a standard Kalman filter with a constant velocity motion and a linear observation model, where we take the delimitation coordinates (u, v, and h) as the direct observation of the state of the object. For each track k (bounding boxes associated to the same identifier ID), we count the number of frames since the last successful measurement association \(a_{k}\). This counter is incremented when  
Figure 4: Multiple object tracking and extraction of the regions of interest (ROIs) or tracks. In these images, pedestrians are detected, and an identification number is assigned to each track. previewing the Kalman filter and reset to 0 when the track has been associated with a measure. Besides, Algorithm 1 describes how bounding boxes associated to IDs are processed in the human detector module. Furthermore, tracks that exceed a predefined maximum age \(A_{max}\) are considered to have left the scene and are removed from the set of tracks. New track assumptions are initiated for each detection that cannot be associated with an existing track. These new tracks are classified as tentative during their first three frames. In the meantime, we expect a successful metric association at each time step. Tracks that are not successfully associated with the measure in their first three frames are deleted. As a result, the mass probability propagates in the state space, and the probability of observation decreases. Intuitively, the association metric should take into account this dispersion of the probability mass by increasing the measurement distance of the track. Counter-intuitively, when two tracks compete for the same detection, the Mahalanobis calculation should be used:  
\[\theta_{i,j}^{1}=\ \left(d_{j}-y_{i}\right)^{T}S_{i}{}^{-1}\left(d_{j}-y_{i} \right)^{T} \tag{1}\]  
where we note the projection of the \(\hat{t}^{th}\) track distribution in the measuring space by \(\left(y_{i}\ ;S_{i}\right)\) and the j-th terminal box detection by \(d_{j}\). Distance promotes more considerable uncertainty because it effectively reduces the standard deviation distance of any detection relative to the projected runway average. This behavior is undesirable because it can lead to increased fragmentation of unstable tracks and tracks in general. Therefore, we used a pairing cascade that gives priority to the most frequently seen objects in order to encode our notion of distributed probability in the association probability. The human detector presented in this article is based on the \"You Only Look Once (YOLO)\" [36; 37; 38; 39] method, which discretizes the output space of selected images into a set of default images of different formats and scales per map location. At the time of the prediction, the network generates scores for the presence of each object category in each default zone and produces adjustments to the area to better match the shape of the object. Also, the network combines the predictions of several feature maps with different resolutions to handle objects of various sizes naturally.