Intersection over Union (IoU) loss function is suitable for: Single-stage object detection Noisy data GAN architecture Imbalanced data
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Step 1: The Intersection over Union (IoU) loss function is a metric used in object detection to measure the overlap between the predicted bounding box and the ground truth bounding box. Show more…
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Text: ConvNets: Object Detection We want to build a safety system for cars that detects pedestrians from a single camera image. We have a large dataset of images, some with pedestrians labeled by a bounding box, and a much larger set of images without any pedestrians. We want to be able to detect and locate pedestrians regardless of their distance and apparent size in the image. In our dataset, the images have a resolution of 900x300, and the height of the pedestrians varies between 30 pixels and 120 pixels, and their width is always less than half their height. (a) You must build a ConvNet architecture for training. What input size would you give it? (b) How would you prepare the data to train the ConvNet, i.e. how would you perform size normalization, cropping, selection of negative examples, class frequency equalization, etc? (c) Building a detector generally requires using a "bootstrapping" method to reduce false negatives. Describe the method. (d) Once the ConvNet is trained, we can use it inside a detection system. Describe the system's overall architecture. Draw a block diagram that includes the major modules, indicating how the system deals with size variations and the possible presence of multiple pedestrians. Give some idea of the ConvNet architecture (no need for a lot of details, but give some information about the network architecture (input size, output size, etc), and the preprocessing and postprocessing.
Sri K.
Tracking systems require several key components: Detector - A model that signals when an object of interest is apparently present and returns a bounding box in pixel coordinates. Tracker - A software module that stores information regarding active tracks and assigns object detections to tracks using a similarity score matrix produced by the Evaluator. Optimally, the Tracker assigns detections to tracks in a manner that maximizes the sum of the similarity scores from each track/detection pair. sij ϵ S indicates the similarity of detection i to track j under some similarity measure. Note that this definition leaves the actual definition of track fairly abstract. In the simplest case, for example, the Evaluator may define sij as the Euclidean distance between the center of the final detection within track j and the new detection i. More sophisticated models may use multiple past detections within a track or model the dynamics of objects (e.g., with a Kalman filter). In this problem, assume that you'd like to produce a novel tracking system. The Detector and Evaluator have already been created, and you must now describe the 0-1 integer linear program that the Tracker will solve to assign detections to tracks. Integer linear programs are constraint problems where the variables are restricted to be integers and are typically NP-hard. In particular, 0-1 integer linear programs restrict the variables further by requiring them to be Boolean (i.e., zero or one). Assume that (a) you are provided with the score matrix S, (b) detections can only be matched to a single track, and (c) tracks cannot be assigned more than once. (a) [5 points] What do the variables in this problem represent? How many are there? CLRS Pages 843-845 is a guide for answering this question.
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