Overview

• Introduction
• Object Classes
  • Deer
  • Hog

Introduction

This dataset is designed to support an object detection task, specifically aimed at identifying and annotating wildlife captured by trail cameras. The dataset includes two classes:

• Deer: Typically identified by their slender legs, antlers (in males), and a graceful neck.
• Hog: Recognizable by their stout bodies, short legs, and distinct snouts.

Object Classes

Deer

Description

Deer are characterized by lean bodies, long and slender legs, and distinctive antlers found on male deer. They have a narrow face and a neck which is generally longer and more proportionate to their limbs.

Instructions

• Draw bounding boxes around the entire visible body of the deer, ensuring antlers (if present) are fully enclosed within the box.
• Include all visible limbs and head; the ears should be considered when visible.
• Do not label shadows or reflections.
• If the deer is only partially visible due to obstacles, extend the box to cover occluded parts, if they are reasonably predictable.

Hog

Description

Hogs are identified by their robust bodies, shorter statures compared to deer, and distinctive snouts. They have shorter legs and a bulky midsection.

Instructions

• Draw bounding boxes around the full body of the hog, including the snout and tail.
• Make sure to encompass the bulk of the body and any visible legs.
• Ignore shadows and reflections.
• If the hog is partially obscured, annotate the visible portion and extend the box based on a reasonable assumption of occluded parts.

Hunting Trail Camera Market Outlook

According to our latest research, the global hunting trail camera market size reached USD 953.7 million in 2025, reflecting robust and sustained demand for advanced wildlife monitoring, hunting scouting, and security technologies. The market is projected to grow at a CAGR of 7.8% during the forecast period 2026-2034, with the market size expected to reach approximately USD 1,887.4 million by 2034. This growth is primarily driven by the increasing adoption of trail cameras for wildlife observation, hunting preparation, and property security, coupled with rapid technological advancements including high-resolution imaging, AI-assisted detection, and cellular network integration.

A significant growth catalyst for the hunting trail camera market is the rising interest in outdoor recreational activities, particularly hunting and wildlife photography. As more individuals, conservation organizations, and professional researchers engage in wildlife study and land stewardship, demand for reliable, high-performance trail cameras has surged considerably. These cameras offer essential capabilities including passive infrared motion detection, multi-shot burst modes, time-lapse recording, and remote monitoring, making them indispensable tools for tracking animal movements and behavioral patterns across diverse ecosystems. The integration of advanced image sensors and on-device artificial intelligence has further enhanced the accuracy and efficiency of data collection, accelerating market expansion through 2034.

Another key driver is the growing emphasis on property and perimeter security across rural and suburban landscapes. Trail cameras are increasingly deployed for surveillance because they operate autonomously in harsh outdoor environments and capture high-quality images even in near-zero ambient light. The proliferation of cellular and wireless trail cameras enables real-time image and video transmission, allowing landowners, farmers, and security agencies to monitor remote locations without constant physical presence. This trend is reinforcing demand across end-user segments that prioritize cost-effective, discreet, and weatherproof monitoring solutions.

Technological innovation remains at the forefront of market growth, with manufacturers continuously introducing models featuring higher-resolution sensors, extended battery life, and more reliable connectivity options. The shift toward cloud-based storage and mobile application ecosystems has simplified remote access to captured media and enabled more sophisticated data analytics workflows. The development of solar-powered wildlife camera systems is particularly noteworthy, as these units extend operational autonomy significantly by harnessing renewable energy, making them ideal for deployment in off-grid or hard-to-reach locations. The growing affordability and availability of trail cameras through multiple distribution channels have also broadened adoption across a wide spectrum of end users, from first-time hobbyists to professional field researchers.

From a regional perspective, North America continues to dominate the hunting trail camera market, accounting for approximately 44.2% of total revenues in 2025. The region's deep-rooted hunting culture, expansive wildlife management areas, and proactive conservation policies create a highly favorable environment for market growth. Europe and Asia Pacific are also gaining significant traction, driven by rising environmental awareness and growing investments in biodiversity conservation and ecotourism. Meanwhile, emerging markets in Latin America and the Middle East & Africa are steadily adopting trail cameras for both recreational and security purposes, presenting new growth avenues for market participants over the coming years.

Product Type Analysis</h2&

This represents raw photo event data from a 1 hectare grid of 27 motion/heat activated camera traps, placed 10m apart. See collection details for more information on methods. The csv file itself contains 18 columns of data which were derived as photos were categorized using the emammal (emammal.org) desktop system. This does not list each photograph, but rather each photo event, which was considered to be an identifiable photo or sequence of photos, in this case of a mammal species, that occurred more than 10 minutes from the end of the last sequence (of the same species at the same camera). Events of humans, birds, and unidentified animals have been removed, as have photo events taken during camera malfunction or displacement. Column definitions should be self-explanatory.

Market size, estimate, forecast and CAGR for the Trail Camera Market Size, Share & Trends Report 2026-2033.

Overview

• Introduction
• Object Classes
  • Deer
  • Hog

Introduction

This dataset consists of trail camera images used for wildlife monitoring. The task involves detecting and annotating two animal classes: Deer and Hog. Each class should be annotated distinctly based on their unique visual characteristics to aid in ecological studies and wildlife management.

Object Classes

Deer

Description

Deer are medium to large-sized animals characterized by their slender bodies, long legs, and typically antlers on males. They often have a distinctive snout and large ears.

Instructions

• Annotate the entire visible body of the deer, including the legs, torso, head, and any visible antlers.
• If a deer is partially occluded by vegetation or structures, extend the bounding box to include the likely position of the occluded parts.
• Avoid annotating if the deer is less than 20% visible, unless its identity is unmistakable.
• Do not annotate reflections or shadows of deer.

Hog

Description

Hogs have a more robust and compact build compared to deer. They have shorter legs, a thick neck, and a distinctively rounded body and head.

Instructions

• Enclose the entire body of the hog, including the head, body, and legs in the bounding box.
• When a hog is partially obscured, include all visible parts and estimate the location for unseen sections.
• Do not annotate if visibility is below 20%, unless the hog is clearly identifiable.
• Exclude annotations of hog shadows or reflections.

The Trail Cameras market, valued at $125.7 million by 2025, projects 6.7% CAGR growth. Analyze market drivers, key segments, and future opportunities. Get data-driven insights.

The Trail Camera Market is projected to reach $109.5 million, growing at a 7.0% CAGR. Understand pixel size and application segments shaping demand. Get data-driven insights.

Trail Camera Detection_v1

## Overview

Trail Camera Detection_v1 is a dataset for object detection tasks - it contains Deer annotations for 3,886 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Discover the booming wildlife camera market! Explore a $241.3M industry with a 5.2% CAGR, driven by conservation, recreation, and tech advancements. Learn about key players, regional trends, and future growth projections in our comprehensive market analysis.

This dataset was originally created by My Game Pics. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/my-game-pics/my-game-pics.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

This QUBES module introduces high school students to the exciting world of field surveys using trail cameras. Designed for environmental science classes, the module offers a hands-on approach to understanding how trail cameras can be used to study wildlife and their habitats. Students will learn essential skills such as selecting survey sites, placing cameras effectively, and identifying local species from the images captured. Through engaging activities and collaborative projects, participants will explore data collection, analysis, and the importance of ethical practices in wildlife observation. By the end of the module, students will have a practical understanding of trail camera technology and its role in environmental conservation, inspiring them to contribute to local wildlife protection efforts.

With continual growth in recreational trail use, it is becoming increasingly complicated to balance demands for outdoor recreation opportunities with wildlife conservation. To better understand how mule deer (Odocoileus hemionus) and Rocky Mountain elk (Cervus canadensis) respond to trail-based recreation, we deployed remote cameras in a paired study design to assess ungulate encounter rates relative to recreation traffic counts and distance from trails. Our methodology allowed us to estimate the magnitude of human activity on public land trails which can be challenging, but is a key step in understanding recreational effects on animal utilization, especially if any interaction in trail proximity and human traffic rates exist. Paired cameras provided a comparison of animal encounter rates on and off-trail at varying trail proximities, and the on-trail camera also yielded daily recreation traffic counts to assess how animals respond to varying traffic for each camera pair. Elk avoided bu..., Detection data from camera trap photographs. Cameras were deployed in paired study design on- and off-trails to simulatenously collect animal and recreation data. This repository includes:

the raw detection data (after images were processed using the CPW Photo Warehouse) (FlatDetectionFile_20210329.csv) R object named 'Daily' prepared for analysis containing daily recreation traffic, daily ungulate encounters, and environmental covariates for each camera (location data removed) (Daily_20250502.rda) , , # Recreational trail traffic counts and trail proximity as a driver of ungulate landscape utilization

https://doi.org/10.5061/dryad.80gb5mkz9

We have submitted our raw detection data (after images were processed using the CPW Photo Warehouse, FlatDetectionFile_20210329.csv) and the prepared dataset (Daily_20250502.rda) at the daily resolution, and used for analysis.

Descriptions

FlatDetectionFile_20210329.csv

Raw detection data after camera images were processed using the CPW Photo Warehouse (UTM identifiers removed).

• GroupID: Integer, ID representing a group of paired camera
• LocationName: Text (String), Concatenated location name containing GroupID and camera distance to trail where zero is the on-trail camera (e.g., "151_0")
• FileName: Text (String), Image filename
• ImageDate: DateTime, Date and time the image was captured (MM/DD/YYYY H:MM:SS, timezone = "MST")
• DetectionID: Text (String), Unique ident...,

Trail Camera Market is projected to grow from USD 105.88 million in 2023 to USD 192.53 million by 2032, at a CAGR of 6.9% in forecast period, 2024-2032.

Remote cameras (“trail cameras”) are a popular tool for non-invasive, continuous wildlife monitoring, and as they become more prevalent in wildlife research, machine learning (ML) is increasingly used to automate or accelerate the labor-intensive process of labelling (i.e., tagging) photos. Human-machine hybrid tagging approaches have been shown to greatly increase tagging efficiency (i.e., time to tag a single image). However, those potential increases hinge on the extent to which an ML model makes correct vs. incorrect predictions. We performed an experiment using a ML model that produces bounding boxes around animals, people, and vehicles in remote camera imagery (MegaDetector), to consider the impact of a ML model’s performance on its ability to accelerate human labeling. Six participants tagged trail camera images collected from 12 sites in Vermont and Maine, USA (January-September 2022) using three tagging methods (one with ML bounding box assistance and two without assistan ...

LILA Camera Traps is an aggregate data set of images taken by camera traps, which are devices that automatically (e.g. via motion detection) capture images of wild animals to help ecological research.

This data set is the first time when disparate camera trap data sets have been aggregated into a single training environment with a single taxonomy.

This data set consists of only camera trap image data sets, whereas the broader LILA website also has other data sets related to biology and conservation, intended as a resource for both machine learning (ML) researchers and those that want to harness ML for this topic.

Species’ distributions are influenced by a combination of landscape variables and biotic interactions with other species, including people. Grizzly bears and black bears are sympatric, competing omnivores that also share habitats with human recreationists. By adapting models for multi-species occupancy analysis, we analyzed trail camera data from 192 trail camera locations in and around Jasper National Park, Canada to estimate grizzly bear and black bear occurrence and intensity of trail use. We documented (a) occurrence of grizzly bears and black bears relative to habitat variables (b) occurrence and intensity of use relative to competing bear species and motorised and non-motorised recreational activity, and (c) temporal overlap in activity patterns among the two bear species and recreationists. Grizzly bears were spatially separated from black bears, selecting higher elevations and locations farther from roads. Both species co-occurred with motorised and non-motorised recreation, however, grizzly bears reduced their intensity of use of sites with motorised recreation present. Black bears showed higher temporal activity overlap with recreational activity than grizzly bears, however differences in bear daily activity patterns between sites with and without motorised and non-motorised recreation were not significant. Reduced intensity of use by grizzly bears of sites where motorised recreation was present is a concern given off-road recreation is becoming increasingly popular in North America, and can negatively influence grizzly bear recovery by reducing foraging opportunities near or on trails. Camera traps and multi-species occurrence models offer non-invasive methods for identifying how habitat use by animals changes relative to sympatric species, including humans. These conclusions emphasise the need for integrated land-use planning, access management, and grizzly bear conservation efforts to consider the implications of continued access for motorised recreation in areas occupied by grizzly bears.

The size of the Cellular Trail Camera market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

Trail Camera Animal Detection

## Overview

Trail Camera Animal Detection is a dataset for object detection tasks - it contains Animals annotations for 1,239 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

AllMammalPhotos_archiveThis file contains a row for each photo image recorded on camera traps during this study. The study was conducted during Summer and Fall months of 2013 and 2014 on the grounds of the Smithsonian Conservation Biology Institute in Front Royal, Virginia USA. All records of birds and humans have been removed. Cameras were established in pairs with a treatment camera (set up with a log in view, or on a game trail) and a nearby random location. End Date refers to the date after which at last one camera in the pair stopped functioning. All photo records from BOTH cameras in the pair taken after this date were removed. The grounds of the study area were divided into grids (500m by 500m) and grids not containing forest were not used. Grid codes are included with each image as is the UTM coordinate of the camera station (UTM Zone 17). "UnderCat" is a three level descriptor for level of understory vegetation at the site. "LogD" refers to log diameter in centimeters, and "Tra...

1. Camera traps are useful for monitoring wildlife populations, but traps may not always trigger when targeting small, nocturnal species. Motion detection techniques have advantages over time-lapse and heat-triggered traps, but need to be deployed to maximize signal-to-noise ratio.
2. As part of a study of flying squirrels (Glaucomys) in urban environments we developed motion detecting camera traps using a raspberry pi microcomputer and camera and a 940 nm IR illuminator on a tree-mounted wooden platform. The system was built from commercially available parts and was comparable in cost to a consumer camera trap, although this cost did not include a waterproof housing. We compared the performance of our system to commercial trailcams.
3. Four pi cameras successfully documented visits by Glaucomys and other animals to bait placed on the platform over three nights at four wooded sites: suburban and rural backyards, a private outdoors club, and a small urban nature reserve (16 camera X site combinations, 48 trap nights). The traps showed low latency, with an average of < 1 night until detection of Glaucomys at each site. Data collected had a high signal-to-noise ratio; of 2182 capture events 55% documented Glaucomys, 40% documented non-target mammals, 1% were caused by large insects, and the remaining 4% were unknown. Commercial camera traps placed at the sites failed to capture many of these events.
4. The low cost and high signal-to-noise ratio of this system may make it easy to adapt for other small animal applications. The main modifications required to deploy this system in new situations will be in locating or providing a fixed or static background against which animals can be observed and in using masking techniques within motion detection software. Methods This dataset describes trailcam images generated from four Raspberry Pi-based trailcam prototypes and associated commercial trailcams as described in the associated MS (Klemens, Tripepi, and McFoy 2021). The data was generated by reviewing all motion detection events generated by the cameras and attributing them to either flying squirrels (Glaucomys sp.), other mammals, insects, or 'other', which includes capture events that cannot be attributed to an organism. The data records the total number of capture events recorded between placement of bait at the camera trap and sunrise, and then attributes each events to one of the four categories.

For some but not all site by camera combinations commercial camera traps were used to observe the Pi cams. The insect category is excluded as trail cams did not create events due to insect movement. The number of capture events in each of the other categories is presented. Percentages presented for the trailcam data represent the percent of capture events recorded by the Pi camera that were also captured by the trailcam.

Location data was collected using a Garmin GPSMAP64ST and the default WGS 84 map datum and WGS 84 map spheroid.