\chapter{Conclusions and Future Work}\label{ch: concl}  
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An application for visual recognition of pedestrians was successfully implemented in this work. \textit{Integral Channel Features} uses a simple and generic framework that takes advantage of the richness of the information present in various channels of an image to produce a number of features capable of distinguishing between the presence and absence of pedestrians through the use of a trained classifier based on AdaBoost. \newline
To validate the approach, experiments were carried out in two distinct datasets. The INRIA dataset served as an important test-bed because of the objectiveness and significance it conferred to the obtained results. In this dataset, the detector achieves a detection rate of almost 80$\%$ on the reference value of 10$^{-4}$ false positives per window, which is in line with many detectors found on literature. It is important to mention, however, that in the publication on which this work was based, a detection rate of 91$\%$ on the same reference value was documented. The explanation for this difference in performance may lie in the fact that the original detector was trained with twice the features that were used in the presented implementation, and it was shown that performance grows rapidly with the number of features utilized. It is safe to assume that if twice the features had been used, a significantly higher detection rate would have been achieved.\newline
Tests were also performed on visual data captured on the \textit{Atlas Car}. A preliminary quantification of the results was done by analysing more than 500 frames acquired in Aveiro. A detector trained solely with INRIA data achieved a detection rate of 42.8$\%$ at the False Positive per Image rate of 0.45, and this detection rate was amplified to 48.7$\%$ just by adding a few hundred hard negative samples from the \textit{Atlas} Pedestrian Dataset to the training data, at the even lower rate of False Positives per Image of 0.33. \newline
In a more qualitative analysis, it was observed that every single pedestrian that was captured in close-range was detected, showing that the method generalizes very well on images of different nature than the ones used for training. The detector also behaves well on mid-range pedestrians given a bright environment. Far-range pedestrians, however, are often missed, which could be partially explained by the fact that the pedestrian windows used for training only contain close and mid-range pedestrians. This problem would probably be minimized if the image was subject to an upscaling prior to analysis, with sacrifices to the computation time. On crowded areas the detector makes usually several hits, but often not individualizing targets, giving more confirmation that the detector is obviously prone to make positive classifications where pedestrians appear.\newline
As one would expect, shadowing and partial occlusion are responsible for most missed pedestrians, as this factors are known to be hard to overcome. Pedestrians on the side-walk are detected less often than pedestrians that are crossing the street, even if they appear in the same range. This odd result can be explained by a slight distortion that occurs on the \textit{Atlas} images but not on the INRIA's, certainly causing the image to have different properties on the borders and more missed pedestrians as a consequence. \newline
The detector also makes a fair amount of false detections, mainly on areas with great density of objects, but it was surprising to find that the vast majority were not random detections at all, but instead followed a consistent and predictable pattern. Most false detections occur on vertical-shaped, pedestrian-like objects, such as trees, traffic signs, lighting poles, mail boxes and other similar objects. This situation not only attests for the validity of the method, since it is obvious that for the most part pedestrian-shaped objects are being detected, but also opens the possibility of the implementation of a post-processing method that could further differentiate these false positives from actual pedestrians. \newline
In fact, there is a wide range of possibilities to improve the framework. Improving the detector speed can be done through the use of a decision structure based on a cascade of boosted classifiers, rather than a single big boosted classifier. This implementation alone would improve the detector speed by multiple orders of magnitude without sacrifice to results (in fact they could be even better). Not only that, a boosted cascade would allow for more features to be evaluated with almost no speed loss, improving detection rate significantly. Still working on the algorithm itself, multi-scale detection over a dense pyramid can be avoided since (Dóllar et al., 2010 \citep{Dollar_et_al._2010}) demonstrated that features can be approximated at nearby scales, thus improving speed even further. Moreover, this algorithm can be even further developed to take advantage of the correlation between detector responses at nearby window positions, a concept introduced in (Dóllar et al., 2012 \citep{Dollar_et_al._2012}) which documents multi-scale detection at 30 FPS. \newline
Other directions can be taken to develop this framework. Using other sensor information such as laser or thermal image to generate possible candidates prior to classification would eliminate the need to analyse the full image, and computation time would decrease exponentially. In fact, multi-sensor detection systems are unavoidable when it comes to robust and reliable detectors, and the work developed in this project is open to that possibility.\newline
As it was shown on by the \textit{Atlas Bootstrapped Detector} results, the data used for training could also be better focused on the application, since the INRIA dataset is rather poor on urban scenarios. So, gathering a more complete dataset highly focused on road situations and use it for training would most certainly greatly improve the detection results on urban environments. \newline
Although this work is highly focused on ADAS, it is obvious that an infrastructure for visual human detection has a wider range of applications. In fact, the work developed in this project is generic enough to be applicable in any field, and one could imagine the usefulness of detecting humans for security, robotics and many other applications. \newline
The greatest contribution of this project was the creation of a framework for pedestrian detection to be incorporated in the \textit{Atlas Car}, a feature that had not yet been developed in the scope of the project. It can and most certainly will be improved in the future, and, although this is a personal opinion, I think that a very robust, fast and reliable detector can be achieved if some of the proposed improvements were to be developed and integrated in the present framework.