You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey 
Chapter Title:  II. OBJECT DETECTION 
1. Abstract of Cited Paper (BIB001):  Deep Neural Networks (DNNs) have recently shown outstanding performance on image classification tasks [14]. In this paper we go one step further and address the problem of object detection using DNNs, that is not only classifying but also precisely localizing objects of various classes. We present a simple and yet powerful formulation of object detection as a regression problem to object bounding box masks. We define a multi-scale inference procedure which is able to produce high-resolution object detections at a low cost by a few network applications. State-of-the-art performance of the approach is shown on Pascal VOC. 
2. Abstract of Cited Paper (BIB002):  Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn. 
3. Abstract of Cited Paper (BIB003):  Deep convolutional neural networks have recently achieved state-of-the-art performance on a number of image recognition benchmarks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC-2012). The winning model on the localization sub-task was a network that predicts a single bounding box and a confidence score for each object category in the image. Such a model captures the whole-image context around the objects but cannot handle multiple instances of the same object in the image without naively replicating the number of outputs for each instance. In this work, we propose a saliency-inspired neural network model for detection, which predicts a set of class-agnostic bounding boxes along with a single score for each box, corresponding to its likelihood of containing any object of interest. The model naturally handles a variable number of instances for each class and allows for cross-class generalization at the highest levels of the network. We are able to obtain competitive recognition performance on VOC2007 and ILSVRC2012, while using only the top few predicted locations in each image and a small number of neural network evaluations.

Literature Review:
Object detection aims to precisely locate interested objects in video frames. Many interesting works have been proposed for object detection by using deep learning algorithms. We review some representative works as follows. Szegedy et al. BIB002 modify deep convolutional neural networks BIB001 by replacing the last layer with a regression layer to produce a binary mask of the object bounding box as illustrated in Figure 3 . Moreover, a multi-scale strategy (see Figure 4 ) is proposed for the DNN mask generation to refine detection precision. As a result, the average precision 0.305 over 20 classes on Pascal Visual Object Challenge (VOC) 2007 can be achieved by applying the proposed network a few dozen times per input image. Different from the Szegedy's method BIB002 , Girshick et al. BIB003 propose a bottom-up region proposal based deep model for object detection. Figure 5 presents an overview of the method. First, around 2000 region proposals are generated within the input image. For each proposal, a large convolutional neural network is used to extract features. Finally, each region is classified by using class-specific linear SVMs. It is reported that using this method can significantly improve detection performance on V OC 2012 by more than 30% in terms of mean average precision (mAP). Similarly, Erhan et al. BIB004 propose a saliency-inspired deep neural networks to detect any object of interest. However, a small number of bounding boxes are generated as object candidates by using a deep neural network in a class agnostic manner. In this work, object detection is defined as a regression problem to the coordinates of bounding boxes. For the training part, an assignment problem between predictions and groundtruth boxes is solved to update box coordinates, their confidences and the learned features by using backpropagation. In summary, a deep neural network is customized towards object localization problem. Object detection has a wide range of smart city applications, such as pedestrian detection, on-road vehicle detection, unattended object detection. Deep learning algorithms are able to handle large variations of different objects. As a result, the smart city systems using deep models are more robust to largescale real data.