OpenCV Test

Consider a complex object detection scenario where an autonomous vehicle needs to detect pedestrians under challenging environmental conditions. You have access to three sensor modalities: RGB camera, thermal infrared, and depth sensor. Your goal is to design a multi-modal neural network architecture that can effectively fuse features from these different sensor inputs to improve pedestrian detection accuracy, particularly in low-visibility scenarios such as night-time or foggy conditions.

Given the following architectural considerations:

- Input dimensions: RGB (224x224x3), Thermal (224x224x1), Depth (224x224x1)
- Baseline single-modal mAP (mean Average Precision) for pedestrian detection:
    - RGB: 68.5%
    - Thermal: 72.3%
    - Depth: 59.7%

Which fusion strategy and network architecture would most likely provide the optimal performance improvement and feature representation?

A: Early Fusion CNN with concatenation of raw input channels followed by shared convolutional layers
B: Late Fusion Ensemble method using separate backbones with weighted majority voting
C: Squeeze-and-Excitation based fusion with channel-adaptive feature recalibration across modalities
D: Feature-level fusion with channel-wise adaptive attention and residual skip connections
E: Parallel multi-stream network with independent feature extractors and a cross-modal transformer fusion mechanism

You trained a Faster R-CNN model on a custom object detection dataset that is both small and imbalanced. Some classes rarely appear, making fair evaluation challenging. You consider: (1) using data augmentation (e.g., random cropping, rotation, photometric distortions) to improve generalization, and (2) adopting class-weighted or specialized metrics to better measure performance on rare classes. Which approach simultaneously enhances generalization to new samples and ensures a more balanced, fair performance assessment across minority classes?

A: Using mAP as the sole evaluation metric
B: Employing weighted AP metrics to highlight minority classes
C: Applying random cropping, rotation, and distortions
D: Increasing anchor sizes only for frequent classes
E: Using Class Weighted IoU
F: Combining data augmentation with a class-weighted metric (e.g., Weighted AP)

You are given two images of a scene taken from different viewpoints, and you want to stitch them together using SIFT feature detection and RANSAC-based homography estimation. You have the following code:

What is the correct way to match the SIFT features between the two images?

A: matches = cv2.BFMatcher(cv2.NORM_L1).knnMatch(des1, des2, k=2)
B: matches = cv2.BFMatcher(cv2.NORM_L2).match(des1, des2)
C: matches = cv2.FlannBasedMatcher().knnMatch(des1, des2, k=2)
D: matches = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE).match(des1, des2)
E: matches = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_FLANNBASED).knnMatch(des1, des2, k=2)
F: matches = cv2.ORB_create().match(des1, des2)

You are given a video sequence of a moving object, and you want to track the object's position and velocity using a Kalman filter. You have the following code:

What is the correct way to detect the object's position in the frame?

A: object_position = cv2.findChessboardCorners(frame, (7, 7))
B: object_position = cv2.detectMinEigenValAndRichterTerm(frame)
C: object_position = cv2.HOGDescriptor().compute(frame)
D: object_position = cv2.goodFeaturesToTrack(frame, 100, 0.01, 10)
E: object_position = cv2.circle(frame, (int(frame.shape[1] / 2), int(frame.shape[0] / 2)), 5, (0, 255, 0), -1)