## 2 Related Work  
Convolutional networks have been employed successfully for object detection and classification [3; 21]. Current models offer methods to classify either a single object's label for a bounding box of a few objects or a whole input window in each scene. The CNNs have also been applied to a wide diversity of other tasks, e.g., stereo-depth [22], pose estimation [23], instance segmentation [24], and much more [25; 26]. In such research methodologies, CNNs are either applied to discover local features or to produce descriptors of discrete proposal regions. Recently, the Fully-Convolutional Network (FCN) [17] has brought a breakthrough to semantic segmentation. It has offered a powerful approach to enhance the power of CNNs by offering inputs of arbitrary size. The proposed network demonstrates remarkable performance on the PASCAL VOC-12 benchmark dataset. A number of outstanding approaches have been presented to enhance and improve the overall efficiency and performance of CNNs. Initially, this idea was used to extend the classic LeNet [27] for digit string recognition. [PERSON] et al. [28] employed Viterbi decoding methods to extract outputs from limited one-dimensional input strings. Later, [PERSON] and [PERSON] [29] improved this idea to expand CNN outputs with a new model of two-dimensional maps. This model was used to detect the four corners of a postal address block. These outstanding developments in FCN have established the strong base for future developments in CNNs for detection. [PERSON] et al. [30] offered a coarse multiclass segmentation CNN for C-Elegans tissues having FCN inference. The proposed network by [PERSON] et al. [16] attains denser score maps within the FCN architecture to predict pixel-wise labels, as well as refine the label map by means of the fully-connected Conditional Random Field (CRF) [31]. Later developments in FCN involve semantic pixel labeling that was initially proposed by [PERSON] et al. named TextonForest [18] and random forest-based classifiers [6]. Emerging progress in FCN and pixel-wise segmentation has offered a huge success in object recognition for an entire image. There are numerous semantic segmentation techniques that are based on classification such as [12; 32], which offered methods to classify multi-scale superpixels into predefined categories. These studies have also combined the classification results for pixel-wise labeling. A number of algorithms [10; 33; 34] classify the region proposals and improve the labels in the image-level segmentation map to attain the absolute segmentation. In semantic pixel-wise segmentation [35] or SfM appearance [36], feature extraction has been explored for the CamVid [37] road scene understanding test. In order to improve the accuracy of the network, the classifier's per-pixel noise predictions were smoothed by pair-wise or higher order CRF [38]. Using the encoder and decoder to enhance the performance is now a key trend in designing the CNN architectures. The idea was initially presented by [PERSON] et al. [39] with an unsupervised feature learning architecture. [PERSON] et al. [15] proposed a deconvolution network, which is composed of deconvolution and un-pooling layers, that offers segmentation masks and pixel-wise class labels for the PASCAL VOC-12 dataset [7]. This work has offered one of the initial successful implementations of deconvolution and un-pooling. With a significant success with encoder-decoder architectures so far, recent CNNs, like SegNet [40] and Bayesian-SegNet [41], have been proposed with a core segmentation engine for whole image pixel-wise segmentation. These are initial models that can be trained end-to-end in one step because of their low parameterization. Recently, ADAS has turned out to be one of the key research areas and demands various methods and tools to improve and enhance the driving experience. Using computer vision and neural networks in ADAS, we can enhance the performance of the auto driving system to a great extent.