Paper - Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis
Key Notes
- Models used - CenterNet and Deep SORT, Detectron2 and Deep
- SORT, and YOLOv4 and Deep SORT
- YOLO and SORT algorithms
- Counting Techniques
- Counting by frame differencing
- Counting by detection
- Motion based counting
- Deep learning based counting
OBJECT DETECTORS
CenterNet
- CenterNet functions on the intuition that if a detected bounding box has a higher Intersection over Union (IoU) with the ground-truth box
- CenterNet is a singlestage detector
Detectron2
Detectron2 supports implementation to multiple object detection algorithms using different backbone network architectures such as ResNET {50, 101, 152}, FPN, VGG16
YOLOv4
You Only Look Once (YOLO) is the state-of-the-art object detection algorithm. New techniques adopted in YOLOv4 are: (i) WeightedResidual-Connections, (ii) Cross-Stage-Partial-Connections, (iii) Cross mini-batch, (iv) Normalization (CmBN), (v) Selfadversial-training, (vi) Mish-activation
EfficientDet
Follows single-stage detectors pattern
SORT
Simple Online and Realtime Tracking (SORT) is an implementation of tracking-by-detection framework where the main objective is to detect objects each frame and associate them for online and real-time tracking application
Deep SORT
A combination of Kalman Filter and Hungarian algorithm is used for tracking
Paper - Tracking Objects as Points
Key Notes
- We track objects by tracking their centers. We learn a 2D offset between two adjacent frames and associate them based on center distance.
- A simple displacement prediction, akin to sparse optical flow, allows objects in different frames to be linked
- Joint detection and tracking
- Early approaches [2, 47] used Kalman filters to model object velocities
Paper - SMOT: Single-Shot Multi Object Tracking
Key Notes
- The first stage exercises a per-frame object detector to localize object bounding boxes in each frame
- The second stage, tracklet generation, merges detection results to create a set of tracklets, i.e., short tracks, based on short-term cues
More Read
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
Object Detection and Tracking in 2020
Paper - ByteTrack: Multi-Object Tracking
Key Notes
- ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation
- TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking
- Multiple Object Tracking with Correlation Learning
In this paper, we identify that the similarity with tracklets provides a strong cue to distinguish the objects and background in low score detection boxes
Data association is the core of multi-object tracking, which first computes the similarity between tracklets and detection boxes and then matches them according to the similarity.
Location and motion similarity are accurate in the short-range matching.
Codes
- People Counter Link1, Link2
- Object Tracking with Opencv and Python, Link2
- Vehicle counting system built with Python/OpenCV, Link2
- GUI Traffic Count using Yolo, Link2
Happy Learning!!!
