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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68251
Volume:
10 |
10 |
Oct 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Department of ECE
CMR Technical Campus
Department of ECE
CMR Technical Campus
Department of ECE
CMR Technical Campus
Department of ECE
CMR Technical Campus
Department of ECE
CMR Technical Campus
Commercial pan-tilt-zoom (PTZ) tracking cameras run detection and servo control on one embedded processor, which delays the response to a moving target and limits them to simple motion-based detection. This paper presents a Vision-Based Tracking Turret that separates the two tasks. An ESP32-CAM streams video over Wi-Fi to a host PC, where a YOLOv5/YOLOv8 model detects the target and computes the angle the turret must move. The angles are sent over a USB serial link to an ESP32 board, which drives two SG90 servos for pan and tilt and switches a laser indicator when the target is locked. The prototype was tested indoors with a single person as the target. In well-lit conditions at 0.5 to 1.5 m the average detection confidence was 0.92 and the detection rate was 98%. The end-to-end delay from capture to servo command was about 80 to 150 ms. The detector, not the actuator, is the main source of delay, so it can be improved by moving inference to a GPU. The hardware costs about INR 1,940 and the design is easy to extend.
KEYWORDS
This paper presented a Vision-Based Tracking Turret in which a host PC runs YOLO detection and an ESP32 board drives the pan-tilt servos and a laser indicator, with an ESP32-CAM as the wireless camera. Splitting detection from actuation avoids the processor contention found in single-processor trackers. The prototype tracked a person indoors with an average confidence of 0.92 at close range and a delay of about 80 to 150 ms, at a hardware cost of about INR 1,940. Future work will add a thermal sensor for dark scenes, Kalman filter prediction to bridge missed detections, GPU or edge-AI inference to remove the PC, multi-target tracking and a web dashboard for remote monitoring.
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Publication Date
10/24/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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